Room: Mission 1

Chair: Peter Lugtig

In this panel, with contributions by the coordinators of the large-scale international surveys in the Netherlands, such as ESS, SHARE, ISSP, EVS, WVS, DPES and GGP. Several large-scale surveys will start with a short talk to illustrate current issues in data collection in the Netherlands for these surveys. After the introductions, a panel discussion and Q&A with the audience will allow for discussion on issues such as how to ensure that hard-to-reach groups are being represented in the large surveys, how to deal with the combination of survey modes in data collection, how to ensure comparability, and how to move towards a future where the different surveys collaborate more in fieldwork.

The International Social Survey Programme (ISSP) – Ruud Luijkx (TiU), Piet Gotlieb (TiU), Tim van Meurs (TiU)

Abstract: In this presentation, we will pay attention to the strict rules imposed by the ISSP secretariat to use an online probability panel and how ISSP collaborates with LISS. Another important issue to be covered is the use of an extensive set of socio-demographic variables.

A Self-Completion Approach for the European Social Survey Tim van Meurs (TiU), Tim Reeskens (TiU), Ruud Luijkx (TiU)

Abstract: Faced with declining response rates and evolving survey environments, the European Social Survey (ESS) has moved toward incorporating online data collection. As part of this strategy, the 2025 ESS will adopt a self-completion approach: alongside traditional CAPI (face-to-face) fieldwork, the survey will also be administered via a sequential self-completion design. Participants will first be invited to complete the questionnaire online (CAWI), with a paper version (PAPI) sent to non-respondents. In addition, as part of broader efforts to harmonize international social surveys, an experimental study is currently underway that integrates the ESS into the LISS Panel, in parallel with the other two data collection strategies (CAPI and CAWI/PAPI). While data collection is ongoing, this presentation will reflect on the challenges encountered during the preparation of this mixed-mode approach (i.e., translation processes, sampling designs, timing of fieldwork, and so on), and explore the implications of self-completion modes for the future of international survey research.

Large-scale RIs in transition: Integration of multiple modes and data sources across countries in SHARE 2.0 data collection – Bella Struminskaya (UU)

Abstract: The SHARE survey is facing the challenge of moving the survey to a furture of mixed-modes. This is challenging in a setting where the oldest old are being interviewed, and physical measurements are at the core of SHARE

European and World Values in the Netherlands: Towards A Renewed Marriage? Mark Boukes (UvA), Ruud Luijkx (TiU), Tim Reeskens (TiU)

Abstract: Fielded for the first time in 1981, the European Values Study (EVS) aimed to investigate the influence of secularization and other postwar changes on individual belaief systems using a cross-national and longitudinal survey design. To broaden this scope, the World Values Survey (WVS) launched a global comparative survey at the end of the 1980s on values and cultural change, which builded on the EVS core methodologies and survey items. Although the two initiatives were originally well integrated, they gradually diverged, eventually resulting in situations where both surveys, using nearly identical questionnaires, were fielded almost simultaneously in the same countries. This presentation shifts focus from historical developments to current preparations and future challenges; outlining the fieldwork underway and the broader goals for data collection and knowledge generation in this next phase of EVS – WVS collaboration.

GGP’s Mixed-Mode Journey – Yuri Pettinicchi (NIDI)

The Generations and Gender Programme (GGP) is at the forefront of implementing mixed-mode data collection in cross-national research. This presentation outlines the progress made so far in transitioning to a mixed-mode approach, drawing on experiences from multiple countries. It reflects on the operational and methodological challenges encountered, the strategies adopted to address them, and the valuable lessons learned that can inform future large-scale, comparative social surveys. 

Room: Mission 2

Workshop leader: Qixiang Fang (Universiteit Utrecht, ODISSEI SoDa Team)

This hands-on tutorial introduces social science researchers to practical applications of large language models (LLMs) for data annotation and analysis. As LLMs such as OpenAI’s ChatGPT become increasingly accessible, they offer powerful tools for automating tasks traditionally done manually—such as coding open-ended survey responses, conducting sentiment or thematic analysis, and extracting structured information from unstructured text. However, effective use of LLMs requires an understanding of both their capabilities and their limitations. This tutorial bridges that gap by guiding participants through the end-to-end process of using LLMs for common social science workflows. Participants will learn how to formulate effective prompts, construct annotation schemes, validate model outputs against human coding, and integrate LLM-assisted analysis into broader research pipelines. The session emphasizes transparency, reproducibility, and critical reflection on model bias and error. Prior programming experience is recommended; tools and examples are accessible as simple Python and R notebooks (see this link). By the end of the tutorial, participants will have the skills to prototype LLM-powered annotation and analysis tasks in their own research, along with a conceptual understanding of when and how to use these models responsibly in empirical social science work.

Room: Progress

Chair: Angelica Maineri

Finding Data for Reuse – Showcasing SSH Data Discovery Platforms in the Netherlands, Ricarda Braukmann (Data Archiving and Networked Services), Jetze Touber (Data Archiving and Networked Services)

Abstract: The first step to reuse existing data is to become aware of its existence. While there are many great and relevant datasets available in the Netherlands, data discovery can still be challenging, limiting the potential that existing data has. Assessing how the Findability of data in the Dutch Social Sciences and Humanities (SSH) community can be improved is hence one of the aspects that the FAIR workstream of the SSHOC-NL project aims to address. In this talk, we would like to showcase the existing data discovery platforms available for researchers in the SSH domain. One of such platforms, the ODISSEI Portal, allows social science researchers to find data from different data providers including survey data, experimental research, and register data available at CBS in one search interface. Similarly, for the humanities, CLARIAH has developed Ineo to provide one entrance to search across available datasets. We present the current features of these data discovery platforms, as well as providing an outlook into steps that the project wants to take to further improve the Findability and discoverability of available data.

Standardizing Access to Sensitive Data with Machine-Actionable Policies, Lucas van der Meer (Erasmus Universiteit Rotterdam), Ahmad Hesam (SURF)

Abstract: The reuse of research data is widely acknowledged as a driver of scientific progress. However, access to sensitive datasets—such as those containing personal, health-related, or proprietary information—remains a significant challenge. In the Netherlands, researchers and data holders face a fragmented and inconsistent landscape of data access policies. Each major data provider, from Statistics Netherlands (CBS) to RIVM and various health registries, enforces its own procedures, legal terms, and review processes. This lack of standardisation creates confusion, lengthy delays, and administrative burdens for all parties involved. Although many data holders are willing to share sensitive datasets under specific conditions, the current system fails to clearly communicate these access conditions in a structured and machine-readable way, as mandated by the Accessibility principle (the A in FAIR). Metadata often lacks critical details about eligibility, permitted uses, and user obligations, making it difficult for researchers to judge whether and how they can gain access. This presentation offers a vision for improving access to sensitive data through the standardisation of access conditions using a machine-actionable language such as the Open Digital Rights Language (ODRL). Based on community consultation with Dutch researchers and data holders, the authors argue that encoding access policies in a digital, interoperable format—supported by user-friendly tools and a coordinating broker service—would lead to a more efficient, secure, and FAIR-compliant system. Such an approach could significantly increase the availability and responsible reuse of sensitive data for research.

FAIR Vocabularies and where to find them, Angelica Maineri (Erasmus Universiteit Rotterdam), Liliana Melgar ( KNAW Humanities Cluster), Menzo Windhouwer (KNAW Humanities Cluster), André Valdestilhas (Vrije Universiteit)

Abstract: FAIR vocabularies help make data interoperable – i.e., they make it possible to exchange and reuse data across different sources. They can be used to structure the data consistently, or to point to meanings of terms as defined by wider disciplinary communities. However, FAIR vocabularies are difficult to find and reuse, especially in the social sciences, where the uptake is still low. During the presentation, we focus on FAIR vocabularies as resources enabling the I principle of FAIR (Interoperability), and explain the difference between the main types of vocabularies (e.g., controlled vocabularies, thesauri, ontologies, etc…) by means of concrete examples. Moreover, we demonstrate the use of the SSH FAIR vocabulary registry, a platform developed by CLARIAH to facilitate the retrieval and reuse of FAIR vocabularies, by making it possible to search, query and inspect vocabularies that are relevant for the field. Next to the Registry, we also present the SSH FAIR Vocabulary Guidelines, namely step-by-step tutorials which guide SSH practitioners not only in finding but also in creating, adapting, and reusing, FAIR vocabularies. By the end of the presentation, the audience will have a better understanding of FAIR vocabularies concepts and applications, and know about the SSH FAIR Vocabulary registry to search vocabularies that are relevant to the SSH field.

Assessing the Quality of Linked Data, An-Chiao Liu (Universiteit Utrecht), Peter Lugtig (Universiteit Utrecht)

Abstract: Linking data from two or more sources is widely used to answer research questions in many fields. The quality of linked data is naturally affected by each data source. These data sources may be subject to selection bias and/or measurement errors, and the quality of the linkage process also plays an important role. If we overlook potential quality issues when analyzing linked data, a misleading conclusion may be drawn. Much research assessing the quality of linked data can be found in the literature. Many of them focus on the amount of the correct linkage or try to quantify the difference between the linked records. For example, match rate or Damerau-Levenshtein distance. However, the exact quality of the inference depends on the research questions in mind. Different types of research questions may reflect different sensitivity to the data quality. In this paper, we discuss possible quality issues of the linked data by giving a general framework. Our framework aims to help individuals interested in data linkage conceptualize potential problems in their studies and provide an overview of existing methods that can potentially detect bias and enhance the quality of linked data. Examples from the humanities and social sciences are shown to illustrate common problems, and some practical suggestions are provided.

Room: Quest

Chair: Peter Gerbands

Which Businesses Answer Surveys? Evidence from Dutch Administrative Data, Jack Fitzgerald (Vrije Universiteit)

Abstract: I leverage a unique administrative register covering the universe of establishments in the Netherlands to examine how characteristics differ between establishments that do and do not respond to business surveys. Only 20% of Dutch establishments responded to regional business surveys in 2022. Responsive establishments employ two fewer people, and exhibit parttime employment rates 15 percentage points higher, than unresponsive establishments. Sectoral response rates can vary by around 50 percentage points, with public-sector and white-collar occupations overrepresented amongst responsive establishments. Solo enterprises registered to residential addresses are the most common kind of establishment, yet exhibit response rates 18 percentage points lower than an average office. However, controlling for contact probability reveals that the majority of sectoral and occupational variation in response rates can be traced back to differences in contact probability rather than responsiveness. These findings highlight generalizability challenges in business surveys and opportunities to improve their design.

Flattening Hierarchies or Amplifying Control? How Frontier Technologies (Re)Shape (De)Centralization within Organizations, Muskan Achhpilia (Radboud Universiteit Nijmegen ), André van Hoorn (Radboud Universiteit Nijmegen), Imtiaz Sifat (Radboud Universiteit Nijmegen)

Abstract: Do information and communication technologies (ICTs) shift decision-making authority from managers to non-managerial employees, or do they reinforce managerial control? Decentralization – the delegation of decision-making authority to lower levels of the organizational hierarchy – is central to harnessing benefits of comparative advantage through specialization. While prior cross-sectional studies suggest that Information and Communication Technology (ICT) investments affect worker autonomy and span of control, the direction of causality remains uncertain: does technology reshape organizational structure, or does organizational structure determine how technology is used? Our paper investigates the evolving relationship between ICT and decentralization, with a focus on maturity and investments in technology over time and organizational context. Using a panel dataset of 28,691 German establishments (1998–2021), we find robust evidence that ICT investments lead to sustained increase in decentralization over time. However, this effect is smaller than prior literature reported once establishment-level fixed effects are accounted for. Notably, the positive effect weakens over time within a firm, indicating that earlier technology investments reshape workplaces and (de)centralization but frontier technology investments may continue to do so at a decreasing rate and may also have possibility in future of (re)centralizing organizations. Moreover, we find that smaller firms tend to be more decentralized, and that digital technologies do not systematically amplify decentralization in megafirms—challenging the assumption that ICT uniformly shifts authority downward across organizational scales. We conclude a nuanced relationship in which technologies can both decentralize and recentralize decision-making, depending on its use, maturity, and organizational context.

Gender diversity and industrial decarbonisation in the Netherlands, Thelma Arko (Universiteit Utrecht), Xuening Tang (Vrije Universiteit), Kattia Moreno (Universiteit Utrecht), Keling Wang (Erasmus MC)

Abstract: Industrial firms are central to the Netherlands’ goal of reducing emissions by 55% by 2030, making it crucial to understand the drivers of industrial decarbonization. While gender diversity is linked to better sustainability practices, its specific role in industrial decarbonization remains underexplored. This study examines whether higher female workforce participation influences decarbonization in Dutch heavy industries, based on the hypothesis that women’s communal traits foster environmental stewardship, especially when reaching a 30% representation threshold. Using a mixed-methods approach, we analysed firms in carbon-intensive sectors (steel, cement, chemicals, petrochemicals) from 2019 to 2024. We developed a three-stage framework: (1) Agenda Ambition Index (AAI) from web-scraped sustainability commitments, (2) Implementation Index (II) tracking concrete actions, and (3) Realised Sustainability Performance (RSP) from emissions data. The key metric, Agenda Realisation Gap (ARG), was calculated as the Mahalanobis distance between ambition and performance. Data sources included LISA employment data, KvK company records, S&P Trucost emissions data, and web-scraped text from over 3,000 companies. We used linear mixed-effects and two-way fixed-effects models with multiple confounder adjustments. Among 552 firms (898 observations), a one-standard-deviation increase in female participation was associated with a 35.5% increase in the ARG (95% CI: -8.79% to 101.4%). Firms with above-median female participation had a 20.6% higher gap than those below the median (95% CI: 5.87%–37.4%). Sectoral differences emerged: manufacturing showed smaller or negative effects (-10.1%) versus other sectors (28.6%). Contrary to expectations, higher female participation correlated with larger ambition-performance gaps, possibly due to women driving more ambitious targets rather than implementation failures. Manufacturing’s distinct pattern may reflect stricter regulatory oversight. These findings highlight the complex, context-dependent role of gender diversity in corporate decarbonization, warranting further research into its influence on both target-setting and implementation.

On the spatial distribution of headquarters across Dutch firms; an explorative analysis using FIRMBACKBONE data, Wolter Hassink (Universiteit Utrecht), Rutger Schilpzand (Universiteit Utrecht), Nicola Sabatini (Universiteit Utrecht), Peter Gerbrands (Universiteit Utrecht)

Abstract: In this presentation, we report on an explorative analysis on headquarter agglomerations in the Netherlands. We make use of data of FIRMBACKBONE, which consists of information at the establishment level on the entire range of firms over the period 2019-2022. We focus on the location of the headquarter at the municipality level, which may provide information on the prevalence of agglomeration effects. Initial findings indicate that the locational patterns of the head quarter depend on the economic sector and the size of the firm (in terms of number of employees). It suggests that headquarter agglomerations are heterogenous with respect to both dimensions.

Room: Royal Lobby

Chair: Lina Kramer

External validation of population-based, multimodal prediction models for wellbeing in a depression and elderly cohort, Dirk Pelt (Vrije Universiteit), Philippe Habets (Amsterdam University Medical Center), Martijn Heymans (Amsterdam University Medical Center), Christiaan Vinkers (Amsterdam University Medical Center)

Abstract: Early identification of individuals at risk for mental health problems, as well as understanding the factors that promote wellbeing, is crucial for prevention and intervention strategies. To this end, we previously demonstrated (Pelt et al., 2024, Nature Mental Health) that wellbeing can be reliably predicted in a general population sample (Netherlands Twin Register; NTR) using psychosocial survey data, with modest contributions from environmental exposures, and no added value from genetic predictors. However, to enable broader, cost-effective applications, it is important to assess whether these prediction models are transportable to other populations, such as clinical or elderly cohorts. This study therefore aims to evaluate the external validity of wellbeing prediction models in the Netherlands Study of Depression and Anxiety (NESDA) and the Longitudinal Aging Study Amsterdam (LASA). This cross-cohort comparison seeks to determine whether models maintain predictive accuracy and to identify both shared and unique predictors across different life stages and mental health contexts. Objective environmental predictors were obtained by linking participants’ postal codes to registry-based exposures, providing information on, for example, air pollution, neighborhood characteristics, and greenspace availability (117 predictors). These were combined with 96 self-reported psychosocial features and 22 polygenic scores (PGS) covering a wide range of domains, and used as input for ordinal gradient boosting models predicting life satisfaction ratings.
Preliminary results on the survey data indicate that models trained on the NTR (ordinal AUC: .80 [.75 – .82] performed similarly in NESDA (o-AUC: .84 [.79 – .88]) but less well in LASA (o-AUC: .68 [.59 – .77]). In addition, the most important features were highly similar across cohorts, including neuroticism, partner status, and self-rated health. Results for models based on environmental and genetic data are forthcoming. These findings support the feasibility of transferring wellbeing prediction models across populations, with implications for developing targeted, personalized mental health interventions.

Do People Know What Breaks Their Hearts?, Fanny Tallgren (Erasmus Universiteit Rotterdam), Bram Wouterse (Radboud University Medical Center), Owen O’Donnell (Erasmus Universiteit Rotterdam)

Abstract: Accurate self-assessment of health risks is critical for optimal individual decision-making and prevention. This paper studies how accurately individuals assess their own risk of developing cardiovascular disease (CVD) and whether they appropriately weigh common risk factors. We link individual-level data from the Dutch LISS panel to longitudinal administrative microdata from Statistics Netherlands (CBS), including hospital discharge records, cause-of-death registers, and medication prescription data. This combined dataset enables us to compare subjective CVD risk perceptions with an objective risk function as well as actual CVD events occurring within five and ten years after the survey. We find that individuals substantially overestimate their CVD risk. However, subjective beliefs are positively associated with realised outcomes. Those who develop CVD on average report probabilities 5.7 percentage points higher than those who do not. We decompose this predictive power and show that most of it is explained by observable risk factors such as smoking, blood pressure, cholesterol, and diabetes. Private information such as family history plays a limited role. Most miscalibration arises from underestimating age-related risk increases. We further analyse how individuals interpret their change in CVD risk over time by comparing their five- and ten-year probability assessments. Despite actual CVD incidence doubling between the two periods, subjective probabilities increase only marginally, suggesting a “flatness bias” in risk perceptions over time. Furthermore, we find that those with lower education and numeracy skills display larger miscalibration, less appropriate weighting of risk factors, and weaker predictive power in their beliefs. Finally, we find that verbal statements about perceived risk (“”I think I am at high risk””) are also slightly misaligned with actual health outcomes. This study contributes to the literature on biases in disease risk perception by combining individual level subjective belief data with rich administrative data on realised health outcomes.”

Tracing Longevity Across Generations: Health and Disease Trajectories in Familial Longevity, Niels van den Berg (Leiden University Medical Center)

Abstract: The genetic component underlying longevity represents key mechanisms contributing to a life-long decreased mortality and morbidity risk. Identifying the mechanisms involved is challenging, mainly because of uncertainty in defining long-lived cases with heritable longevity amongst phenocopies and complex gene x environment interactions. Hence, we investigated the longevity trait and its transmission from one generation to the next. In large-scale family-tree data from Utah (UPDB) and the Netherlands (LINKS), we studied 20,360 unselected families containing index persons, their parents, siblings, spouses, and children, comprising 314,819 individuals. We found strong evidence that longevity is transmitted as a quantitative genetic trait among the top 10% survivors of their birth cohort. The survival advantage amounted to 31% for individuals with top 10% surviving first and second-degree relatives in both databases and across two generations, even in the absence of non-long-lived parents. Subsequently, we developed the Longevity Relatives Count (LRC) score as an instrument to quantify the number of long-lived family members and observed that the survival advantage of study participants increased with each additional long-lived family member. Applying the LRC score to the LLS (Netherlands) and SEDD (Sweden; register data) showed that an increasing number of long-lived ancestors associates with an increasing delay in disease incidence (Fig1). As compared to their partners, members of long-lived families have a delayed onset of medication use, multimorbidity and blood-based profiles indicating improved metabolic health and low inflammation in mid-life. Our results indicate that an increasing number of long-lived ancestors marks a decade of healthspan extension, healthier metabolomics profiles, and can be used for more optimized case definitions. Building on these findings, future work will refine and generalize the LRC score into a broader family-based survival metric, enabling wider application across existing studies and supporting the disentanglement of gene x environment interactions in healthy aging and longevity.

The Absence of Genetic Risk for Cardiovascular Disease in Exceptional Survival (Longevity), Pedro Ferreira (Leiden University Medical Center), Marian Beekman (Leiden University Medical Center), Niels van den Berg (Leiden University Medical Center)

Abstract: Aging is the major risk factor for chronic diseases. Unlike the general population, members of long-lived families maintain exceptional health as they age. Healthy survival to extreme ages (longevity) clusters within families. However, research has not yet elucidated the underlying mechanisms of longevity. There are two important reasons for this: 1) the group with the highest heritability is often not studied and 2) the hypothesis-generating nature of most genetic studies requires larger study cohorts. In our previous work, we showed that members of the longest-lived families have a 10-year delayed onset of their first chronic diseases. We therefore hypothesize that the absence of genetic predisposition to chronic diseases is one of the key-mechanisms involved in longevity delayed disease onset. We investigated this hypothesis in the Leiden Longevity Study, a cohort with data from more than 400 long-lived families in 3-generations. To analyze our data, we constructed a set of PRSs covering the top 10 causes of death in the Netherlands. We observed that descendants of long-lived families have lower genetic risk for cardiovascular disease (CVD). Using accelerated failure time modeling, we further showed that around 20% of the delayed cardiovascular disease incidence in long-lived families is explained by CVD common genetic variants. We conducted gene-annotation enrichment analysis of the SNPs in the CVD PRS using DAVID and observed seven significantly enriched clusters. Finally, we constructed a novel cholesterol PRS based on the cholesterol metabolism cluster which significantly predicted time to all-cause mortality in a 90+ study population, covering 19 years of follow-up. Our study indicates that common variants related to cardiovascular diseases and cholesterol metabolism contribute to healthy aging. Furthermore, we demonstrate that investigating SNPs, identified in well-powered Genome Wide Association studies, associated with longevity-related endophenotypes can provide insight into the genetic architecture of the longevity phenotype itself.

Room: Expedition

Chair: Daniel Oberski

Citizen-in the-loop in training and updating machine learning models for smart data, Barry Schouten (Centraal Bureau voor de Statistiek), Chris Lam (TU Eindhoven), Marco Puts (Centraal Bureau voor de Statistiek)

Abstract: Accurate and timely training data are a crucial prerequisite to (supervised) AI/ML methods. For some AI/ML applications training data can be collected at relatively low cost. However, often an investment is needed in evaluating, annotating and checking events of interest. Apart from costs, annotations and corrections may not be feasible without the involvement of the data subjects. We consider official statistics AI/ML (re)training data settings where the help of ‘citizens’ is imperative to obtain accurate training data and to keep training data up to date. As a secondary motivation, especially in the official statistics context, we see added value in involving general populations in terms of transparency, engagement and trustworthiness. Citizen-in-the-loop strategies are an interplay between a statistical sampling viewpoint and a methodological annotator recruitment viewpoint. The sampling viewpoint aims at efficacy of citizen involvement, i.e. AI/ML performance. The annotator viewpoint considers annotator psychology in terms of burden and competence. Together they lead to a focus on efficiency: how to reach and maintain sufficient performance while not overloading or overburdening citizens. The main elements in our strategy are a distinction of different citizen involvement phases and a trade-off against in-house annotation by staff. We see four phases: feature selection, baseline training, targeted training and updating. Each requires different citizen involvement. The decision as to which annotation queries may be allocated to citizens is set against the proportion which are to be allocated to in-house staff. A key role in the trade-off is annotator bias, i.e. measurement errors made in the annotations. Major methodological challenges are representation and drift in features and concepts. We evaluate ideas and tactics at the hand of case studies in the area of smart surveys and machine learning applied to the resulting smart data.

Being certain about the uncertain: Prediction intervals with missing data, Florian Van Leeuwen (Universiteit Utrecht), Thom Volker (Universiteit Utrecht), Stef van Buuren (Universiteit Utrecht)

Abstract: The development and application of prediction models is often complicated by missing data. Most statistical methods do not readily allow for incorporating missing values during estimation. Consequently, the model of choice cannot be estimated. Imputation can be a solution: the missing values are replaced by values drawn from an imputation model, after which the prediction model can be estimated. Broadly, two strategies can be defined: single and multiple imputation. With single imputation, each missing cell is filled in with the most probable value given the other variables in the data, under some model. Historically, single imputation has been the method of choice in a prediction context. It is simple, readily fits in subsequent workflows and may not necessarily harm predictive accuracy. However, if the interest is in accurate predictive uncertainty quantification, single imputation is problematic. Attempting to reconstruct the missing value precisely is often naïve, and by doing so, the distribution of the data is heavily distorted. Important, the variability in the data is reduced, resulting in uncertainty estimates that are too small. We propose a multiple imputation-based workflow that yields valid uncertainty estimates Multiple imputation accounts for the uncertainty around the missing values by drawing imputations from a distribution. The uncertainty in the imputations is propagated through the prediction model, and the scale of this uncertainty can be used to quantify the prediction uncertainty using Rubin’s rules. We show that, using this approach, valid prediction intervals can be constructed in a linear model setting, regardless of whether missing data occurs in training or testing data, or both. Moreover, the prediction interval is valid regardless of the amount of missing data, and is individualized, in the sense that it scales with the amount of missing data an individual record has. We implemented the required estimation method in the R-package MICE.

Filling the Gaps Through Amplified Asking: Combining Register and Survey Data to Understand Carbon-Intensive Time Use, Maike Weiper (Universiteit Utrecht), Javier Garcia-Bernardo (Universiteit Utrecht), Erik-Jan van Kesteren (Universiteit Utrecht), Jiamin Ou (Universiteit Utrecht)

Abstract: Statistical institutions collect rich register data and administer valuable surveys, but the latter often cover only a small portion of the population. While combining register and survey data already holds great promise, a new range of opportunities emerges when we can also combine multiple surveys – even when they lack (substantial) overlap. This research project explores how we can leverage Dutch register data to impute survey variables for individuals outside the original sample, thereby effectively expanding the scope of survey-based research.
Our initial focus is on time-use data, a uniquely informative but resource-intensive type of data, typically gathered through detailed diaries that are impractical to collect at scale. We are developing a method to generate realistic estimates of how individuals across the Netherlands spend their time, using demographic and household-level register data. This approach may make it possible to link time-use data to other survey domains, such as income, health, or consumption, and thus study previously inaccessible patterns, including intersectional forms of gender inequality or individual-level carbon emissions. In this presentation, we will introduce a preliminary approach for producing such large-scale time-use estimates by combining various sources of register data. As the project develops, particular attention will be given to addressing methodological challenges, including the generalizability of predictions across social groups, potential biases in imputed data, and the broader implications of applying such methods in survey research. Ultimately, the project aims to develop a scalable and responsible framework for expanding the reach of survey research. By enabling new combinations of survey content and increasing representativeness, we hope to contribute to more inclusive, data-driven understandings of societal dynamics in the Netherlands.

A Two Step Approach for Modeling Total Error with Multi-source Data, Santiago Gómez-Echeverry (Vrije Universiteit), Arnout van Delden (Centraal Bureau voor de Statistiek), Ton de Waal (Centraal Bureau voor de Statistiek), Dimitris Pavlopoulos (Vrije Universiteit)

Abstract: The expansion of administrative and big data and the increase in the survey’s non-responses have highlighted the relevance of assessing the quality of non-probability samples. To tackle this issue, people usually resort to the Total Error (TE) framework, which divides the error into a measurement and a representation component. Extensive literature focuses on measurement error, often using a combination of data from different sources to evaluate whether the observed variables adequately capture the concept intended to be measured. Another branch of the literature has centered on the representation error, assessing how respondents are selected in the sample, leading to systematic differences between the population and the observed units. However, research modeling both of these components simultaneously is still scant. In the present study, we address this gap by jointly modeling the measurement and the representation errors, combining recent advances in both areas. We conducted a simulation study to evaluate our TE model under different specifications of measurement and representation errors. Additionally, we performed a case study analysis using a combination of Italian administrative registers and the Labor Force Survey (LFS) to evaluate the TE in the income variable. Our preliminary results show that our model adequately captures the different error sources and provides a good strategy for assessing the TE when using a combination of probability and non-probability data.

Room: Mission 1

Chair: Marcel Das

Building a research app for data collection using smartphone sensing for the social and behavioral sciences, Bella Struminskaya (Universiteit Utrecht), Thijs Carriere (Universiteit Utrecht), Niek De Schipper (Universiteit Utrecht), Laura Boeschoten (Universiteit Utrecht)

Abstract: The data from smartphone sensors and wearables have the potential to transform social and behavioral science research by ensuring in-the-moment, longitudinal, rich, precise, and scalable data collection. Researchers are increasingly taking advantage of such technologies using smartphone research apps with a wide range of sensing functionalities, including ecological momentary assessment, geolocation sensing, physical activity sensing, app usage, and others. Numerous apps and platforms exist that researchers can use for data collection. They vary in functionalities, needed IT know-how, and possibilities for re-use. In this presentation, we provide a comprehensive review of existing apps and platforms used for app-based data collection in social and behavioral sciences and official statistics based on a scoping review of existing platforms for app and sensor-based data collection. Based on this, we discuss methodological, operational and software-development considerations for mobile sensing research infrastructures. Secondly, we introduce an open-source smartphone app research infrastructure under development that will be available to social science researchers in the Netherlands and being developed by the authors with the support of the Dutch Research Council (NWO). We present the conceptualization of data collection modules (geolocation, EMA, physical activity, smartphone app use, etc.), features of providing participant feedback and engaging participants, back-end organization for users (i.e., researchers) and features that allow conducting randomized experiments for users. We share our experiences of software development and ensuring how the infrastructure can be developed in a sustainable manner and be integrated into national and international social science research landscapes. We focus on integrating research-based methodological aspects of privacy protection and participant engagement in software (i.e., research app) development, and discuss more general aspects of maintenance, integration with other parts of the research landscape, sustainable funding, as well as legal and ethical considerations when creating socio-technological systems for data collection.

Laura Boeschoten (Universiteit Utrecht), Niek De Schipper (Universiteit Utrecht), Theo Araujo (Universiteit van Amsterdam)

Abstract: The Digital Data Donation Infrastructure (D3I) is a collaborative initiative among six Dutch universities. The primary aim of D3I is to facilitate data donation for research in the social sciences and humanities in the Netherlands. D3I enables individuals to securely and transparently donate their digital trace data for academic research purposes by making use of their rights granted under the General Data Protection Regulation (GDPR). D3I empowers researchers to work directly with individuals to collect data, thereby bypassing the need for platform-mediated data access. This opens new avenues for studying human behaviour and platform dynamics.
To facilitate that researchers collect digital trace data in a safe, ethical and efficient way, we have developed a comprehensive research infrastructure that enables researchers to design a complete data donation study, considering consent, local processing of the digital traces, integration with other data collection approaches, participants invitations and data storage, while also providing recommendations regarding methodological and ethical aspects.
This infrastructure has been in place since 2023 and has been used by more than 20 research projects in and outside the Netherlands. The infrastructure continues to be expanded, improved and further embedded within the wider Dutch research infrastructure landscape for social sciences. In this presentation, I will first explain the sustainable development model that we use for this infrastructure, that facilitates the continuous development and improvement through multiple funders. Second, I will showcase how researchers both inside and outside the Netherlands interested in collecting digital trace data for their research purposes can use this infrastructure, where I cover topics such as participant recruitment, informed consent, data storage and re-use of data donation datasets.

A historical disease database of the Netherlands and based on newspapers using natural language processing techniques, Kristina Thompson (Wageningen University), Qixiang Fang (Universiteit Utrecht), Erik-Jan van Kesteren (Universiteit Utrecht)

The nineteenth and early twentieth century Netherlands had a very high burden of infectious diseases. Outbreaks of diseases such as tuberculosis and smallpox were common. To study health and living standards in this period, capturing the disease burden is critical. While today we are able to measure the disease burden with infection rates, this is not possible for the past. In the Netherlands, there are no available measures of the disease burden prior to the mid-twentieth century, aside from mortality rates. Mortality rates are also not perfect proxies of disease: they fail to capture diseases that negatively impacted an individual throughout their life, but did not kill them. This prevents researchers from, for instance, studying how exposure to infectious diseases in early life might impact someone’s health in later life. An indicator of the infectious disease burden that is not based on mortality information would enable researchers to do so. In the nineteenth and twentieth centuries, Dutch newspapers reported on the number of new cases and deaths from specific infectious diseases. A large share of these newspapers are digitized and publicly available via the Royal Library’s Delpher.nl. This project aims to convert these newspaper mentions into a quantitative indicator of the disease burden. To do so, we leverage natural language processing methods to: process newspaper mentions of a disease, connect them to a geographical region, map them onto numeric scales, and assess their performance as a proxy of the disease burden in a given municipality and year. We have thus released an open-source disease database and several historical maps. Further, we show how this disease database can be used by researchers, and how to account for measurement uncertainties in the disease database.

Room: Mission 2

Workshop leader: Tom Emery

Abstract: During this session, datasets that are available via SANE, such as LISS and FIRMBACKBONE, will be demonstrated, including what type of linkage and enrichment can be done with this data in a secure environment. You will also be taken through the data application process and how to launch a SANE environment on your machine, request additional memory or compute, and ensure that your SANE project is well documented and reproducible.  You will need to bring your own laptop to this session.

Room: Progress

Chair: Yuliia Kazmina

Using Prediction Gaps and Registry Data to Understand the Unequal Influence of Social Contexts on Educational Outcomes, Javier Garcia-Bernardo (Universiteit Utrecht), Eva Jaspers (Universiteit Utrecht), Weverthon Machado (Universiteit Utrecht), Samuel Plach (Boconni University)

Abstract: Social contexts—such as families, schools, and neighborhoods—shape life outcomes. The key question is not simply whether they matter, but rather for whom and under what conditions. Here, we argue that prediction gaps—differences in predictive performance between statistical models of varying complexity—offer a lens for identifying surprising empirical patterns (i.e., not captured by simpler models), highlighting where theories succeed or fall short (interactions or complex). Using population-scale administrative data from the Netherlands, we compare logistic regression, gradient boosting, and graph neural networks to predict university completion from early-life contexts. Overall, prediction gaps are small, suggesting that previously identified indicators, particularly parental socioeconomic status, capture most of the measurable variation in educational attainment. However, gaps are larger for disadvantaged students, indicating that the effects of social context for these groups go beyond simple models in line with sociological theory. Our paper shows the potential of prediction methods to support sociological explanation.

Simulating the influence of social networks and food environments on the formation of health-taste beliefs, Jiri Kaan (Wageningen University), Yara Khaluf (Wageningen University), Sonja Kunz (University of Vienna), Kristina Thompson (Wageningen University)

Abstract: Health-taste beliefs – the belief whether healthy eating requires compromising on taste – can significantly influence food choices and is found to vary by socioeconomic status (SES). Lower SES individuals tend to believe more frequently that healthy foods are less tasty, potentially contributing to health inequalities. Yet, the mechanisms driving these SES differences in health-taste beliefs remain poorly understood. Based on previous work, we propose that two factors shape health-taste beliefs differently across SES groups: social network influences (health-taste beliefs of close ties) and food environment exposures (skewed availability of unhealthy and tasty foods). To test this, we developed an agent-based model using data from 1046 participants in the LISS panel from the Netherlands. Agents preferentially connected in a scale-free network based on their socioeconomic status and were initialized with observed sociodemographic and dietary behaviors. Over time, agents updated their health-taste beliefs through simulated social network interactions and food environment exposures. The simulated health-taste beliefs were validated against the health-taste beliefs observed in the LISS panel dataset. Our simulations suggest that lower SES agents were more susceptible to social network influences compared to higher SES agents. This differential susceptibility suggests that social network structure and dynamics may perpetuate differences in health-taste beliefs across SES. These findings have significant implications for how health interventions are designed. Traditional interventions may attempt to directly change lower SES groups’ health-taste beliefs via education; however, our model suggests that targeting social networks may be more effective.

Community-level networks on a societal scale, Rense Corten (Universiteit Utrecht)

Abstract: The emergence of online social networks like Facebook in the early 2000’s promised a breakthrough in social networks research social network research by enabling analysis of societal-scale interactions due to abundant data. However, despite many groundbreaking studies, progress has been limited by a lack of freely available data. In rare cases where such data have been made available by platforms to researchers, individual-level data can typically not be shared with the wider research community. However, data that are aggregated to higher social entities, such as municipalities, can be shared more easily. This paper presents one such data set based on the (now defunct) Dutch social network platform Hyves. From an underlying individual-level network covering a significant fraction of the population, we create a data set consisting of topological features of within-municipality networks, covering all municipalities in the Netherlands. This provides a unique insight into features of social connectivity within municipalities that is not readily available from other resources. We present descriptives of topological features of municipality networks, explore associations with other properties of municipalities, and demonstrate the usefulness of municipality-level network measures for social research.

Room: Quest

Chair: Tim Reeskens

Understanding Controversiality in Online Discourse: A Multi-Method Analysis of the DerStandard, Elena Candellone (Universiteit Utrecht), Emma Fraxanet (Universitat Pompeu Fabra), Darja Cvetkovic (Institute of Physics Belgrade), Simon D. Lindner (Complexity Science Hub)

Abstract: Controversial discussions drive online discourse by attracting user engagement and shaping public opinion. Depending on how these conversations are framed and moderated, they can either support healthy democratic debate or increase toxicity and polarization. Understanding and quantifying controversiality is crucial for identifying sources of disagreement, influential narratives, and antagonistic dynamics. Previous studies have examined controversy on online platforms from various angles, such as partitioning user interaction networks. However, to our knowledge, no approach integrates ideological positioning, network structures, and thread dynamics. To address this, we analyze a decade-long dataset from the Austrian newspaper platform DerStandard, comprising over 75 million user comments and 412 million up- and down-votes. This German-language corpus offers a rare opportunity to study controversy at scale beyond English-language platforms, with structured user interactions, topic context, voting behavior, and threaded discussions. Our work has two main components. First, we examine whether latent ideologies can be extracted from user interactions with selected influential users—identified based on positive, negative, and controversial vote patterns. We define controversiality using a metric that favors a balanced volume of positive and negative votes. Correspondence analysis (CA) provides user embeddings, and we validate influencer classifications using large language models (LLMs). The network structure reveals tightly connected clusters with internal alignment and polarized cross-cluster interactions. Second, we assess thread-level controversy through characteristics like thread depth, number of posts, negative votes, and a controversy index inspired by the h-index. We also examine response times, the original author’s controversiality score, and polarization based on ideological stance. Preliminary results show that structural thread measures strongly correlate, while vote-based metrics capture distinct facets of controversy. Our work highlights how controversy manifests in online communities, contributing insights to debates on polarization and content moderation.

The construction of territorial stigma: mapping representations of the Bijlmer, Gijs Custers (Erasmus University Rotterdam)

Abstract: Newspaper media play a dominant role in (re)producing and circulating negative representations of neighborhoods. This study will focus on the Bijlmer, one of the most well-known and ‘notorious’ areas in the Netherlands. The aim of this study is to show how the Bijlmer is portrayed in newspaper media, how these representations change across time and what might explain these changes. A large corpus is build including articles on the Bijlmer from the five largest newspapers between 1990-2024. Computational methods like topic modelling and sentiment analysis are used to analyze this corpus. This presentation will focus on the work in progress, outlining some of the potential benefits and challenges relating to corpus building and textual analysis. The study aims to unravel how and why areas become (de)stigmatized.

Analyzing Country-Level Heterogeneity in Public Attitudes Toward Migration Topics on Bluesky Jisu Kim (Universiteit Utrecht), Jiho Kwak (Seoul National University), Egor Kotov (Max Planck Institute for Demographic Research), Tom Theile (Max Planck Institute for Demographic Research)

Abstract: Migration is a globally significant political issue, with discourse varying across countries. This study examines the regionally varying patterns of migration discourse on Bluesky, an emerging platform that saw an increase in number of users following Elon Musk’s acquisition of Twitter (now X) and its subsequent policy changes. We explore how Bluesky users discuss migration issues in the United States, the United Kingdom, and Germany, focusing on key topics, sentiment, and variations in word usage. To achieve this, we identify country-specific themes, sentiment dynamics, and construct dialectograms using embeddings trained on posts from each country to explore nuanced linguistic differences. During this process, since Bluesky lacks geolocation metadata, we employ a Large Language Model (LLM) to annotate posts based on the country they reference. Our findings reveal distinct themes shaping Bluesky users’ migration discourse in the United States, the United Kingdom, and Germany. In all countries, negative sentiment posts outnumber positive sentiment posts approximately five times, and the predominant themes of negative sentiment posts were more partisan compared to those of positive sentiment posts. A bilateral comparison using dialectograms highlights words co-occur distinctively with words related to recently relevant subtopics in each corpus. This study provides new insights into regional variations in migration discourse on Bluesky and introduces an innovative approach for analyzing social data sources that lack traditional metadata.”

Room: Royal Lobby

Chair: Christoph Janietz

How Neighborhood and School Contexts Jointly Shape Primary School Outcomes in Dutch Cities, Dieuwke Zwier (European University Institute), Herman van de Werfhorst (European University Institute), Carla Haelermans (Maastricht University)

Abstract: Neighborhoods and schools are both seen as important extrafamilial social contexts shaping children’s education: advantaged environments offer greater access to various resources, whereas exposure to concentrated disadvantage can limit life chances. A pressing question is how neighborhood and school contexts are interconnected, and jointly shape educational outcomes. Residential and school choices are intertwined: some (prospective) parents choose where to live to access desirable schools, or choose schools depending on where they live (e.g., attending a local school or “opting out”). Subsequently, (the degree of) these interconnections may shape contextual effects. Yet, despite theoretical recognition of these links, empirical research on school and neighborhood effects has largely developed separately. This paper examines how neighborhood and schools jointly shape (inequality in) educational outcomes in the Netherlands. Using geo-coded longitudinal register data from the four largest Dutch cities (Amsterdam, Rotterdam, The Hague, and Utrecht), we study how families sort into neighborhood-school structures and how this shapes children’s track recommendations in the transition to secondary school – a consequential moment in the Dutch system. We use a recently developed two-step approach that explicitly accounts for contextual selection processes, and compare multiple urban regions to assess variation across socio-spatial contexts. Results will be presented at the conference.

Trends in inequality of opportunity: studying heritability of education over time, Marjolijn Das (Centraal Bureau voor de Statistiek), Mayke Nollet (Vrije Universiteit)

Abstract: Inequality of educational opportunity and its trend over time is one of the most prominent policy concerns in Western countries. Inequality of educational opportunity can be defined as a situation where children with similar innate capabilities have different educational outcomes as a result of their different (socio-economic) circumstances, which contributes to social stratification. Intergenerational transmission of education is often used as an indicator of inequality of opportunity. However, shared genes are a major source of resemblance between parents and children, and studies on intergenerational transmission are unable to distinguish between genetic effects and the influence of the family environment. Twin studies are designed to parse out the effects of genes versus the shared environment by making use of the difference in genetic relatedness of monozygotic versus dizygotic twins. Twin studies are usually based on survey data. The current study uses population-wide data on twins and siblings of Dutch origin from integral registers of Statistics Netherlands, birth cohorts 1964-1997. These integral data allow us to assess detailed trends over in genetic influence (‘heritability’) and the influence of the shared environment on educational attainment in the Netherlands. We adapt the classical twin design to estimate heritability in the absence of information on zygosity. Over these thirty-four years, heritability of educational attainment increased and the influence of the shared environment decreased. Hence, educational attainment has become increasingly determined by innate capabilities, which strongly suggests a decrease in inequality of opportunity in these birth cohorts of Dutch origin. This study contributes to insight in contemporary patterns of stratification. Future research should focus on younger cohorts and non-Dutch origin groups.

School Competitiveness And The Development of Motivation: A Longitudinal Cohort Study On Social Inequalities In Secondary Education, Nathalie Aerts (Universiteit van Amsterdam)

Abstract: The role of competition in educational contexts is usually viewed critically, especially among socio-economically vulnerable groups. Yet, more recent insights show that extrinsic motivators, such as performance goals, can also have a positive effect on motivation and achievement, particularly for students who start of less intrinsically motivated. Students with lower SES tend to respond more strongly to extrinsic motivators, such as competitiveness, because they start off less intrinsically motivated on average and consider school less personally meaningful. For this group, a competitive learning environment may actually function as an incentive to activate engagement and interest. In this study, we investigate whether school competitiveness (peer climate, school type and level) contribute to the motivation development in the early years of secondary education, focusing on differences by SES. We use data from the Onderwijsmonitor Limburg (OLM), a large-scale longitudinal study among almost all school in South Limburg. Students are followed throughout their entire educational trajectory, with regular measurements of motivation, achievement, SES, class and school characteristics. In a four-wave design, whether performance goals (T1), caused by school competitiveness, lead to increased interest (T2) and ultimately better performance (T3), controlling for baseline motivation and other relevant factors. We test whether these pathways are stronger for lower SES students, for whom external stimuli are hypothesized to be an entry point for engagement. Linking OML to data from the National Cohort Study on Education (NCO) allows us to continue follow pupils beyond their primary education. In doing so, this contribution not only provides theoretical insights into motivation development, but also policy-relevant starting points for equity and effect learning environment in secondary education. 

Room: Expedition

Chair: Ana Macanovic

Foundation Models for Life-course Modeling, Flavio Hafner (eScience Center), Tanzir Pial (Stony Brook University), Ana Macanovic (European University Institute), Lucas Sage (Sorbonne University)

Abstract: By conceptualizing life events—such as education, employment, and familial milestones—as sequences of temporal markers, we build machine learning models inspired by sequence models from natural language processing. Using Dutch registry data and a vocabulary where each token represents a life event attribute, we pre-train this model with language modeling objectives akin to Masked Language Modelling, aiming to create life-course embeddings for diverse sociological predictive tasks. Preliminary results demonstrate the embeddings’ utility in predicting outcomes such as income levels and fertility decisions. Future work will move in two directions. First, we will explore improvements in model architecture to include a person’s wider life context, such as information about their neighbors, co-workers, and firm identifiers, during training. Second, we will explore the extent to which these models are foundational, i.e., whether their embeddings are generally predictive of outcomes in social science datasets.

Using Interpretable Network Embeddings to Understand Populist Voting Behavior in Population-Scale Registry Networks, Malte Lüken (eScience Center), Javier Garcia-Bernardo (Universiteit Utrecht), Sreeparna Deb (TU Delft), Flavio Hafner (eScience Center)

Abstract: Applying machine learning to administrative registry data has challenged the limits of predicting social outcomes. In deep learning, prediction models often rely on embeddings that project complex input data into a latent numerical space. In the social sciences, embeddings can be used to compress social networks, which reduces their dimensionality while preserving information about the network structure. Registry data can be used to construct social networks at population-scale by creating ties between persons who belong to the same administrative entity (e.g., households, neighborhoods). Such ties are understood to represent social opportunities, reflecting potential interactions and shared environments. Using registry data from Statistics Netherlands, we created person-level embeddings for the entire Dutch population solely based on social opportunity ties in five domains . We demonstrate the usefulness of these embeddings by predicting right-wing populist voting in the Dutch parliament election 2023, linking embeddings with data from a representative survey. While embeddings alone showed a predictive signal, they performed worse than established individual covariates of populist voting. Moreover, they did not improve predictions when combined with said covariates. To make the embeddings more interpretable, we applied regularizing auto-encoders that disentangle the embedding dimensions, enforcing sparsity and orthogonality. We found that one of the regularized embedding dimensions was highly predictive of right-wing populist voting. From this embedding dimension, we created a weighted version of the population network that revealed differences in network structure between higher- and lower-educated persons. These structural differences can help explain right-wing populist voting decisions. We see this study as a starting point to create interpretable population-scale social representations that researchers can use to relate social structure and social outcomes. The embeddings are available to the Dutch research community through the Data Storage Facility by Statistics Netherlands and ODISSEI.

Are births predictable with register data? Evidence from the Predicting Fertility data challenge, Elizaveta Sivak (Rijksuniversiteit Groningen), Gert Stulp (Rijksuniversiteit Groningen)

Abstract: Accurate predictions of life outcomes can inform social theory and policy. Yet, in contrast to predictive success in fields like biology, social science predictions often perform poorly. Why are life outcomes so difficult to predict? One common explanation is small sample sizes of social datasets. We examine this in the context of fertility by studying the predictability of having a child within three years under conditions that are close to the best currently possible. We use full-population data from the Dutch registers and high-quality LISS survey data in the Predicting Fertility data challenge. LISS data includes a wider range of theoretically relevant variables, including subjective measures such as fertility intentions. Administrative data provide vast, high-resolution coverage of life-course trajectories but lack subjective measures. This unique framework enables us to leverage the strengths of these datasets to assess the current predictability and contrast them to learn more about what limits predictability and gain insights about fertility behaviour. Over 150 people participated in the data challenge and submitted over 70 models, ranging from traditional machine learning approaches to state-of-the-art foundational models. Despite the administrative data’s scale and detail, predictive performance remained modest: even the best models fell substantially below the theoretical upper limit of predictability caused by randomness inherent in conception and fetal survival. Survey-based predictions performed slightly better. Analysis of the most important predictors across survey- and register-based models suggests that this difference may reflect the absence of certain key variables in administrative data. Still, the improvement was small, indicating that register data likely captures most major determinants of fertility. These results suggest that accurately predicting individual fertility remains highly uncertain, even in the short term and with extensive data. Modest predictive accuracy is unlikely to stem primarily from limited sample size, but may reflect the inherent unpredictability of life outcomes.

Room: Mission 1

Chair: Jessica Piotrowsk

Public Responses to Jihadist Terrorism on Social Media, Christian Czymara (Nederlands Interdisciplinair Demografisch Instituut)

Abstract: In recent years, several major terror attacks linked to political Islam have shaken Europe. Based on social media data, this study examines how public reactions to Jihadist terrorism are expressed and shaped to explore the formation of public views of immigration and intergroup relations. The theoretical predictions derived from combining terror management theory, group threat theory, and the concept of social resilience, are empirically tested using validated Keyword-Assisted Topic Modeling (keyATM). Unlike traditional topic modeling approaches, keyATM incorporates pre-specified keywords, enhancing both the precision and interpretability of results while mitigating the impact of researcher subjectivity. I analyze over 100,000 time-stamped and geo-coded Tweets on immigration and related issues across four languages in the week following eleven major Islamist terrorist attacks in nine European cities posted by users located in the city of the attack. Consistent with the theoretical predictions, the findings reveal that both threat-related and tolerance-related topics emerge prominently across all four languages. However, deeper analyses uncover temporal shifts in the discourse: While initial reactions often reflect nationalist and exclusionary views, later stages show a rise in inclusionary and tolerance-oriented topics. These results highlight the dynamic nature of social media debates on immigration issues after dramatic events, where initial threat-driven responses often give way to more inclusive and tolerance-oriented discussions as time progresses, underscoring its role in shaping and reflecting the evolving public response to terrorism.

Detecting Asymmetrically Polarized Group Dynamics on Reddit, Laima Baldina (Rijksuniversiteit Groningen), Russell Spears (Rijksuniversiteit Groningen), Hedy Greijdanus (Rijksuniversiteit Groningen), Leah Henderson (Rijksuniversiteit Groningen)

Abstract: Political polarization research increasingly recognizes group identification as a key driver of partisan divides. Social identity approach shows that group identification creates comprehensive psychological alignment over and above shared opinions. This alignment should produce observable group-level dynamics like boundary maintenance, norm development, and coordinated behavior patterns. Yet we have limited evidence of how these collective processes actually unfold over time in real political contexts, shaping both the groups themselves and their members’ experiences. Due to the high level of detail, resolution, and precision, social media data enables investigation of these group processes simultaneously with tracking individual behaviors. Reddit’s architecture provides a particularly instrumental context for studying partisan group behavior due to distinct community boundaries, anonymous interaction, and collective rating system, which amplify group dynamics and make them easier to detect. We analyzed ten years of Reddit data (2012-2021) from five major U.S. political communities, developing computational measures to track group-level behavioral dynamics. For each partisan community, we calculated monthly cross-participation rates in the shared forum r/politics and analyzed how these communities’ contributions were rated by the broader public there. We find systematic asymmetries in how partisan communities evolved their engagement with shared political discourse. Left-wing communities maintained stable participation in r/politics throughout the decade, while right-wing communities showed progressive withdrawal interrupted by temporary mobilization during major political events. Analysis of comment ratings revealed corresponding patterns, where posts by right-wing community members received increasingly negative evaluations from the broader r/politics audience over time, while left-wing members’ posts maintained ratings similar to the community baseline. These findings reveal self-reinforcing dynamics between group participation and reception that shape polarization’s asymmetric evolution. By capturing group-level behavioral mechanisms as they naturally unfold, this computational approach reveals the complex collective dynamics underlying political polarization in naturalistic settings.

Digital visibility and spatial inequality: uneven representation of urban hotspots on TikTok, Shuyu Zhang (TU Delft), Claudiu Forgaci (TU Delft), Lei Qu (TU Delft), Maarten van Ham (TU Delft)

Abstract: In contemporary cities, social media has become an important intervention in the spaces people perceive, imagine and experience. The identification of urban ‘hotspots’ is no longer limited to tourist guides or official planning discourses, but it is increasingly driven by the interplay between platform algorithms and user-generated content. Studies have noted the impact of social media on spatial structure and urban inequality. This study proposes and quantifies an indicator of ‘social media place visibility’ for Amsterdam hotspots on the TikTok platform, revealing how social media reshapes the spatial inequality structure of urban places. By collecting and analysing a large dataset of TikTok posts hashtaged Amsterdam from 2020 to 2023, and applying geo-parsing, spatial mapping, visualisation and place-hashtag network analysis, this study examines the uneven representation of urban hotspots on TikTok. The findings show that the platform hotspots weaken the traditional centre-periphery spatial structure to a certain extent, making peripheral areas such as De Pijp, West and Zuid ‘unexpected hotspots’ due to their high visibility. At the same time, social media platforms have created new inequalities in reconfiguring visibility. The study found that places dominated by consumer activities, such as food and drink, were reinforced on TikTok, while a large number of other urban areas were digitally marginalised, creating a new uneven representation of urban activity spaces and spatial inequalities. In addition, the spatial discourse constructed by the platforms poses a challenge to urban governance, posing the risk of unpredictable consumption behaviours and spatial appropriation. Overall, this study demonstrates how digital platforms have become new actors in the production of urban space, emphasising that in the age of social media, understanding urban spatial inequalities must go beyond the physical to a deeper focus on algorithmic power and the politics of visibility.

Room: Mission 2

Chair: Kasia Karpinska

Generate synthetic tabular data in a transparent, understandable, and privacy-friendly way. Raoul Schram (Universiteit Utrecht), Erik-Jan van Kesteren (Universiteit Utrecht)

Abstract: Social scientists often handle highly privacy-sensitive information, such as mental health questionnaires, personal income data, or social media information. This makes it ethically and legally problematic for the researcher to share the real data publicly as part of the research process. Among other issues, this can create reproducibility problems: while the analysis code for the research might be published, other researchers cannot reproduce the results without the original data. One solution that can improve the situation is to create a synthetic copy of the original data and publish this synthetic copy. However, generating “safe” synthetic data is not trivial, as it does not automatically protect privacy. There are many methods to create synthetic data, but often it is not transparent how much information is actually retained in the synthetic data. We will present the python package metasyn, which can generate synthetic data in a transparent, understandable and privacy-friendly way. In contrast to most other synthetic data software, we make the explicit choice to strictly limit the statistical information in our model in order to adhere to the highest privacy standards. In the presentation, we first explain the basic principles of metasyn in contrast to other synthetic data methods, after which we show how easy it is to use metasyn in a real-world reproducible social science data workflow. Finally, we present our disclosure control plug-in, which adds an additional layer of security to the synthetic data based on well-known official statistics guidelines for publishing data products. Metasyn is currently funded through a TDCC-SSH grant on synthetic data, which is a collaboration between many different organizations, including Utrecht University, Maastricht University, DANS and SURF. We will show how metasyn contributes to this project and how it will benefit national infrastructure organisations, and talk about the progress in the implementation for these organisations.

Running online behavioral experiments on the SURF Research Cloud, Rense Corten (Universiteit Utrecht), Sandor Spruit (Universiteit Utrecht), Rob Franken (Universiteit Utrecht)

Abstract: Behavioral experiments have become increasingly popular in the past decades across a variety of social science disciplines, including economics, political science and sociology, because such experiments allow to study causal social mechanisms in great detail. When implemented in web-based interfaces, behavioral experiments can be deployed in a flexible manner in a diversity of contexts (e.g., embedded in a survey or in lab-in-the-field settings) or be scaled up to study complex social dynamics in large groups. However, setting up such online experiments is challenging to the typical researcher, as it requires appropriate software tools, programming skills, access to subjects, and hardware to securely and robustly host online experiments. This project aims to address the latter barrier. We present a method to easily set up and up and run a virtual server to host experiments based on the popular open-source platform oTree, levering the computational resources available via the SURF Research Cloud. We demonstrate how a custom-built SRC component can be used to quickly and easily set up a dedicated virtual oTree server, reducing the need for researchers to rely on commercial services or custom-built local hardware solutions.

AmCAT (Amsterdam Content Analysis Toolkit), Sofia Gil-Clavel (Univeristy of Amsterdam), Wouter van Atteveldt (Univeristy of Amsterdam)

Abstract: A key challenge to open science practices, such as sharing and reusing data, is that many researchers consider it a burdensome afterthought rather than a core part of the research itself. The AmCAT  (Amsterdam Content Analysis Toolkit) addresses these needs by providing an open collaboration environment for discovering, accessing, analysing and (re)using textual data. It also facilitates non-consumptive research when full data access is impossible. Combining a user-friendly GUI with a powerful API, AmCAT ensures researchers with varying computational skills can participate. AmCAT aims to bring collective benefit by offering an open source and decentralized solution for storing and analysing potentially sensitive documents, decreasing dependence on commercial or foreign providers and their terms of use throughout the research life cycle. 

Room: Progress

Chair: Konrad Turek

From Full-Time to Part-Time: Long-Term Maternal Employment Trajectories After Childbirth in the Netherlands, Flora Zhou (Erasmus Universiteit Rotterdam), Tom Emery (Erasmus Universiteit Rotterdam), Jennifer Holland (Erasmus Universiteit Rotterdam)

Abstract: The Netherlands has one of the highest rates of part-time employment in the world, particularly among mothers. While prior research often attributes maternal part-time work to childbirth, a key puzzle remains: when does this shift to part-time employment begin? Is it temporary or permanent? And what factors influence whether mothers return to their pre-birth employment levels? Existing studies have largely focused on short-term or cross-sectional patterns, leaving long-term trajectories underexplored. Using administrative data from Statistics Netherlands, this study applies multilevel linear spline models to track maternal employment trajectories over the 10 years following first childbirth. Unlike traditional non-linear models, linear spline models use piecewise linear segments joined at knots to reflect discontinuities or shifts in slope at key time points. The analysis identifies four distinct stages. The first stage (Year-2 and Year -1) indicates that a high proportion of Dutch mothers worked full-time at the pre-birth stage. The second stage (Year-1 – Year1) indicates that maternal employment sharply decreased during childbirth. The third stage (Year1-Year5) presents that employment continues to decline as the dominant pattern becomes a 3-day (approximately 0.6 FTE) workweek. The last stage (Year5-Year10) presents a very small increase in maternal employment, but it remains well below pre-birth levels. The 3 days working patterns are still dominant. The results also suggest that the number of childbirths and early daycare usage affect the slope of employment at the last stage. Contrary to the common assumption of a V-shaped recovery in maternal employment, our findings suggest a permanent shift: Mothers in the Netherlands tend to adopt and maintain a long-term part-time work pattern following their first childbirth.

New skill adoption and cities, Harm-Jan Rouwendal (Rijksuniversiteit Groningen), Anet Weterings (Planbureau voor de Leefomgeving), Sierdjan Koster (Rijksuniversiteit Groningen)

Abstract: This paper studies the relationship between new technology adoption and agglomeration economies. We asses to what extent the adoption of new technologies differs across space by studying differences in the adoption of new digital skills in the Netherlands. The use of online job vacancy data allows us to study the implementation of new technologies on the skill level in the workplace and empirically analyse the spatial variation in new digital skill adoption within occupations. Results show that employers in urban areas more often demand new digital skills for open jobs compared to similar jobs elsewhere. This indicates new technology adoption is higher in cities, which helps explain the productivity premium of urban areas.

Should I work part-time? Evidence from a Dutch Survey Experiment on Individual Preferences and Societal Norms, Simona Cicognani (Universiteit Leiden), Lea Hauser (Universiteit Leiden), Natalia Montinari (University of Bologna)

Abstract: This study examines individual preferences and perceived social norms regarding part-time work in the Netherlands, a country characterized by one of the highest female part-time employment rates globally. We run a vignette-based survey experiment embedded within the LISS panel dataset, a nationally representative longitudinal survey. Respondents (1,769) were randomly assigned to one of four treatment groups that varied along two dimensions: the protagonist’s gender (female or male) and parental status (with or without a child), in a between-subject design fashion. The study measures first-order beliefs (FOB), representing respondents’ personal preferences about whether the vignette character should reduce working hours, and second-order beliefs (SOB), capturing respondents’ perceptions of societal norms regarding the same. Our results show that both personal and perceived social norms favor reduced working hours more strongly for female protagonists and when a child is present. Importantly, perceived social norms (SOB) reflect that gender and parenthood interact to influence expectations, meaning their combined effect is stronger than each alone. In contrast, personal beliefs (FOB) treat gender and parenthood as having separate, additive effects. Contrary to expectations, female respondents express more conservative personal norms than males, favoring greater reductions in working hours, especially for female protagonists with children. Furthermore, respondents whose mothers worked full-time are less likely to support reduced working hours, suggesting intergenerational influences on work preferences. The analysis of perceived gender pay and pension gaps reveals that women perceive larger disparities than men, but these perceptions do not significantly affect part-time work preferences. Awareness of the negative financial consequences of part-time work predicts stronger support for increased working hours, although this effect does not differ by respondent gender. Overall, these findings highlight how both personal beliefs and perceived social expectations, shaped by gender and parental status, influence attitudes toward part-time work in the Netherlands.

Room: Quest

Chair: Pearl Dykstra

Family networks in the transition to parenthood: A predictive machine learning approach, Nicolás Soler (Erasmus Universiteit Rotterdam), Tom Emery (Erasmus Universiteit Rotterdam), Agnieszka Kanas (Erasmus Universiteit Rotterdam)

Abstract: The transition into parenthood implies making choices about childcare that have critical implications for parental employment trajectories, child development, and gender equality. Previous work has studied how childcare choices are influenced by the availability of both formal and informal family care. The latter is typically conceptualized in terms of the availability of support from the paternal or maternal grandparents. However, such perspective disregards how formal childcare uptake is also influenced by the family obligations and resources afforded by a larger set of kin, including both sets of grandparents, as well as aunts, uncles, and cousins. To fully grasp the role that family plays in parents’ childcare strategies, there is a need to adopt a multidimensional perspective that moves beyond simple measures of grandparental presence or geographical proximity to family. We provide a more comprehensive test. We adopt a machine learning approach where we quantify the importance of care availability by predicting whether first-time heterosexual parents will use formal childcare services in the Netherlands. We use register data that allows to measure parental characteristics, the location and capacity of nearby formal childcare providers, and a comprehensive set of characteristics of the family network of children, disaggregating their kin by age, gender, and lineage. The use of machine learning allows us to model multidimensionality while maintaining interpretability through the use of Shapley values. We further interpret the results by studying whether predictive performance and the relative importance of predictors varies across parental educational status. Put together, we provide novel evidence of the role of family care availability on parenting strategies and formal childcare uptake, as well as on how these are stratified by educational status.

Nature-nurture beliefs and parenting, Tim Wienand (Erasmus Universiteit Rotterdam), Hans van Kippersluis (Erasmus Universiteit Rotterdam), Niels Rietveld (Erasmus Universiteit Rotterdam)

Abstract: A long-standing question in the study of child development is to what extent genetic endowments (“nature”) and family environment (“nurture”) influence human capital formation. While extensive research underscores the importance of both dimensions, less is known about individuals’ beliefs regarding their relative importance. At the same time, parents’ perceptions about the process of child development are known to play an important role for their parental investment decisions. Bridging these two strands of literature, we design a survey experiment to investigate whether parental beliefs about the role of genetic endowments for children’s educational achievement influence parental educational time investment. To identify the causal effect of beliefs about the importance of genetics, we incorporate an information experiment in our survey that randomly informs participants about different published heritability estimates of children’s performance at the end of primary school. We then measure a range of outcomes relevant to parental investment decisions. Specifically, we develop new hypothetical vignettes to assess respondents’ views on optimal levels of parental time investment and its and allocation between siblings, perceived returns to ability and parental investment, and attitudes toward equality of schooling outcomes and parental investment between siblings. To validate responses to the hypothetical scenarios, we also ask questions to the subsample of parents related to their own children. The survey was administered in June 2025 as part of the LISS panel, a nationally representative longitudinal household survey in the Netherlands. We aim to present initial findings of our experiment at the ODISSEI conference 2025.

Gender Differences in Fertility Narratives Based on Neural Topic Modelling: Perspectives from Dutch men and women, Xiao Xu (Nederlands Interdisciplinair Demografisch Instituut), Anne Gauthier (Nederlands Interdisciplinair Demografisch Instituut), Gert Stulp (Rijksuniversiteit Groningen), Antal van Den Bosch (Universiteit Utrecht)

Abstract: The impact of gender roles on fertility decisions with distinct perspectives shaped by societal and family roles and their perceived responsibilities. In this study, we examine gender-based differences in where and how individuals express their concerns about fertility intentions and uncertainty in the Netherlands. We use open-ended textual responses collected both in the LISS panel and in the Dutch round of the Generations and Gender Programme (GGP) in 2023. By applying the BERTopic, a state-of-the-art transformer-based topic modeling approach, to thousands of open-ended survey responses, we uncover narrative structures that reflect the differences in underlying beliefs, priorities, and concerns related to childbirth plans by men and women. Our results reveal that gender continues to shape not only fertility plans but also the discourses and attitudes surrounding the reproductive decision-making process.

Room: Royal Lobby

Chair: Fabio Tejedor

Quantifying social tipping potential: Empirical evidence from three European countries, Christine Hedde-von Westernhagen (Universiteit Utrecht), Francesco Pasimeni (Eindhoven University of Technology), Floor Alkemade (Eindhoven University of Technology)

Abstract: Social tipping dynamics offer a promising mechanism to accelerate net-zero transitions. These dynamics involve rapid, often nonlinear shifts in social-environmental systems, triggered by feedback mechanisms and small interventions, with lasting effects. When change in one domain catalyzes transformation in others, tipping cascades may emerge, reinforcing systemic change. While recent conceptual work has outlined potential tipping domains, empirical evidence remains limited, particularly regarding household energy behaviours. This paper presents empirical analyses of social tipping potential in household energy behaviours across three European countries: Germany, the Netherlands, and the United Kingdom. We developed a new cross-national dataset based on representative surveys conducted in early 2025, including around 3,300 respondents in total, of whom 300 are members of energy communities (ECs). The survey examines energy behaviours, preferences, and attitudes—such as adoption of rooftop solar, electric vehicles, and heat pumps—as well as broader lifestyle practices, views on climate change, and socio-demographic characteristics. Our three-step analytical approach begins by identifying clusters of co-occurring energy behaviours. We then estimate the causal effect of EC membership on these behavioural profiles using econometric matching techniques. Finally, we simulate changes in the population distribution of energy behaviour clusters under different policy and intervention scenarios.
A first descriptive analysis indicates notable differences between countries and groups. For example, the share of households in the general population having adopted or considering the adoption of heat pumps is substantially higher in the Netherlands (60%), as compared to Germany (45%) and the UK (40%). In contrast, EC members in the UK have a higher share of adoption or adoption intention (70%) than EC members in Germany (52%) or the Netherlands (63%). Our inferential analyses outlined above will provide a nuanced account of these differences to help identify the country-specific drivers and barriers to whole-system change in the household energy sector.

Reshaping Economic Segregation in the Netherlands: The Role of Social Housing Allocation (2007–2023), Diego Buitrago (TU Delft), Clementine Cottineau (TU Delft)

Abstract: This paper evaluates the impact of targeted social housing allocation on spatial income segregation in the Netherlands. In 2010, the Dutch government introduced the “90% rule,” requiring housing associations to allocate the vast majority of social rental units to households below an income threshold. While the policy was designed to improve targeting and reduce competition with private landlords, it also altered the spatial distribution of low-income households by constraining access to other neighborhoods. Using detailed administrative data covering the universe of households over a 16-year period, this study assesses how the policy reshaped the geographic allocation of poverty across Dutch cities. The analysis focuses on micro-level changes at the 500-meter grid scale, allowing us to identify localized effects beyond broader national or demographic trends. We disentangle policy effects from compositional and structural forces, and employ an instrumental variable strategy to estimate the causal relationship between changes in household composition and spatial concentration. The results suggest that even income-based policies not explicitly designed as spatial interventions can generate strong place-based effects. These findings contribute to ongoing debates in urban economics on the spatial consequences of non-price housing allocation mechanisms and the trade-offs between targeting efficiency and spatial equity.

The Economic Urban Divide: A Detailed Study of Income Inequality and Segregation in Dutch Urban Areas (2011–2022), Javier San Millán (TU Delft), Clémentine Cottineau-Mugadza (TU Delft) , Maarten Van Ham (TU Delft)

Abstract: Research on segregation and economic inequality is often limited to major capitals and conurbations, neglecting smaller cities. This oversight can lead to public policies based on insights that may not be universally applicable. Leveraging geo-coded register data, this study addresses this problem in the case of the Netherlands by computing income inequality and residential segregation annually in all urban areas from 2011 to 2022. Contrary to most literature, this paper shows that inequality and segregation have remained stable or decreased in most cases. In addition, when looking at how income is distributed among social segments, how segregated they are, and at which geographical scale segregation occurs, we find significant variation between urban areas. More unequal urban areas also tend to be more segregated, but patterns vary, and the same segregation levels can coexist with diverse inequality metrics. Four groups of urban areas are identified through a cluster analysis.

Room: Expedition

Chair: Kevin Wittenberg

Measuring Complexity and Reproducibility: A Comprehensive Benchmark of LLMs for Multilingual Policy Agenda Topic Annotation, Bastián González-Bustamante (Leiden University)

Abstract: Although Large Language Models (LLMs) have rapidly expanded the text-as-data toolkit available to social scientists, their performance remains highly sensitive to task complexity, prompting, language and model parameters. In order to assess these limits, we conduct a cross-lingual benchmark of 79 contemporary LLMs (e.g., OpenAI’s GPTs and o-series, Claude models, xAI’s Grok, Meta’s Llama, Alibaba’s Qwen, Mistral models, among others) on a demanding zero-shot classification task: labelling one of the 21 major topics in the Comparative Agendas Project codebook to bills and Acts drawn from Danish, Dutch, English, French, Hungarian, Italian, Portuguese and Spanish corpora. We then conducted a meta-analysis that shows reasoning models lift F1-scores by around nine percentage points on average. In comparison, reproducible models under deterministic deployment conditions incur a cost of about eight percentage points in F1-score. In addition, preliminary and ongoing fine-tuning experiments suggest that fine-tuned transformers may surpass the best zero-shot LLM classification performance. These findings quantify the trade-off between performance and reproducibility, highlighting chain-of-though reasoning as the most effective option for complex policy agenda annotation. They also set a baseline for future venues, where systematic fine-tuning and alignment strategies could be used to test whether some models can close the performance gap while retaining full reproducibility.

Care Work and Code Work: Can Language Models Read Between the Lines?, Elisabet Doodeman (Vrije Universiteit), Arjen de Wit (Vrije Universiteit), Dominik Meier (University of Basel), John Mohan (Third Sector Research Centre)

Abstract: Can LLMs distinguish intentions to care for family, friends, and unknown others? We explore this question with text answers to an open-ended survey question. In 2008, a national UK survey asked 9,760 participants, all born on the same day and year, to describe their imagined life at age 60. These open-ended responses offer a rich but underexplored opportunity for computational text analysis, particularly for identifying intentions to volunteer or care for others within broader life narratives. Notably, participants were not prompted to discuss volunteering or care, reducing the influence of social desirability bias and allowing these themes to emerge in more naturalistic and varied ways. We explore the use of large language models (LLMs) as research assistants in coding these responses, with a focus on detecting care-related themes. What began as a technical investigation quickly raised deeper conceptual questions: What does it mean to care? What qualifies as “care” in the context of future-oriented self-descriptions? What seems obvious to a human coder may be ambiguous to a model, and vice versa. How can we communicate socially constructed, value-laden concepts to an LLM in a way that preserves meaning and nuance? Our experiments with several models culminated in promising results using Llama 3.3 70B Instruct, with precision and recall metrics higher than 0.9. We reflect on both the model’s performance and the broader methodological implications of using LLMs for interpretive tasks in social science. This work contributes to emerging conversations on the role of AI in qualitative analysis, highlighting challenges in alignment, abstraction, and conceptual agreement between humans and machines.

Trying to be human: Linguistic traces of stochastic empathy in language models, Bennett Kleinberg (Tilburg University), Jari Zegers (Tilburg University), Jonas Festor (Tilburg University), Stefana Vida (Tilburg University) 

Abstract: Differentiating between generated and human-written content is important for navigating the modern world. Large language models (LLMs) are crucial drivers behind the increased quality of computer-generated content. Reportedly, humans find it increasingly difficult to identify whether an AI model generated a piece of text. Our work tests how two important factors contribute to the human vs AI race: empathy and an incentive to appear human. We address these aspects in three experiments: human participants and a state-of-the-art LLM wrote relationship advice (Study 1, n=530) or mere relationship descriptions (Study 2, n=610), either instructed to appear as human as possible or not. New samples of humans (n=428 and n=408) then judged the texts’ source. Our findings show that when empathy is required, humans excel. Contrary to expectations, instructions to appear human were only effective for the LLM, so the human advantage diminished. Study 3 showed that these effects persist when the instructions are targeted at avoiding the appearance of an LLM. Computational text analysis (Study 4) revealed that LLMs become more human because they may have an implicit representation of what makes a text human and apply these heuristics. The model resorts to a conversational, self-referential, informal tone with a simpler vocabulary to mimic stochastic empathy. We discuss these findings in light of recent claims on the on-par performance of LLMs.