ISA World Congress 2027 | Call for Abstracts

This session examines the methodological, epistemological, and practical implications of using AI agents as research tools in sociology. It invites contributions addressing how large language models, Retrieval-Augmented Generation (RAG) systems, and agent-based architectures are reshaping data collection, corpus building, qualitative coding, text classification, summarization, interpretation, and validation in social research.
The session is organized around three axes: First, it considers LLMs and RAG as research infrastructure, focusing on tokenization, embeddings, retrieval quality, evidence-anchored generation, and the risks of hallucination. Second, it examines AI agents as systems that combine language models with tools, memory, and semi-autonomous action, enabling new forms of distributed research agency. This includes applications in large-scale studies of disinformation ecosystems, as well as the emerging use of AI agents as simulated focus groups capable of generating deliberative discourse, disagreement, and perspective-taking. Third, the session addresses critical limits: where human judgment remains non-delegable, how tool design shapes methodological outcomes, and how human-in-the-loop, human-on-the-loop, and human-out-of-the-loop models affect validity, accountability, and interpretation.

The session welcomes empirical applications, methodological frameworks, and critical reflections, especially contributions from early-career researchers and scholars from the Global South.

Session Organizer:
Leonardo NASCIMENTO FERNANDES, Federal University of Bahia, Brazil, leofn3@gmail.com

This session examines how Machine Learning (ML), Large Language Models (LLMs), and related AI systems are reshaping social-scientific research. These tools are increasingly used for many analytical tasks like text annotation, qualitative coding, discourse analysis, summarisation, classification, and synthetic data generation. At the same time, they raise important methodological, epistemological, and ethical questions about validity, transparency, reproducibility, and the delegation of interpretive authority, ever so relevant in respect of the increasing adoption and usage of AI systems in general.
We invite contributions that explore how ML and LLMs can be integrated into quantitative, qualitative, and mixed-methods research designs to support and innovate social research. Relevant topics include the use of AI to analyse unstructured data, support different aspects of scientific discovery, and assist with interpretive workflows or the modelling of complex phenomena.

The session also welcomes work that treats AI systems themselves as objects of empirical investigation. This includes model inspection methods such as auditing, probing, prompt experiments, scenario-based testing, bias and fairness evaluation, comparisons across models, model versions, and human subjects or annotators, and analyses of how AI systems respond to social identities, political frames, and cultural contexts.

By bringing together empirical, methodological, and critical perspectives, the session aims to reflect on the role of ML and LLMs in research and the social world, promote interdisciplinary dialogue, and advance methodological innovation with and through emerging technologies.

Session Organizers:
Dario CHIANESE, University of Naples Federico II, Italy, dario.chianese@unina.it
Francesco AMATO, University of Naples “Federico II”, Italy, francesco.amato2@unina.it

Administrative data have become central to the social sciences and to the social indicators used to track populations, allocate resources, and evaluate policy. Generated by states and institutions in the course of administration, they offer scale, continuity, and near-complete population coverage, which reduces concerns about representativeness and allows individuals to be followed over time. In turbulent times, when indicators are used to diagnose crises and justify interventions, what is recorded, how it is recorded, and who or what remains missing become central to understanding governance and the production of social knowledge. Because these data are produced for administrative rather than research purposes, how well they measure social phenomena is an important and increasingly relevant question.
This session focuses on methodological and substantive problems that arise when administrative records are used to describe and explain social outcomes: measurement error, inconsistent definitions across time and space, changing classifications, coarse or one-dimensional categories, outdated records, selective coverage, linkage error, and overinterpretation of statistics shaped by policy rules and reporting practices. These problems limit comparability across cohorts, periods, institutions, and jurisdictions, and may leave some populations absent, misclassified, or only partly recorded.

We welcome empirical, methodological, theoretical, and comparative papers that examine these issues, particularly in research on labor markets, social assistance, healthcare, education, and taxation. Contributions may combine administrative data with surveys, qualitative research, or other sources. The session considers how these data can be used for research across the social sciences, and what they can and cannot show.

Session Organizers:
Ondrej BUCHEL, Institute for Sociology of the Slovak Academy of Sciences, Slovakia, ondrej.buchel@savba.sk
Matěj BĚLÍN, Institute of Social Policy, Ministry of Labour, Social Affairs and Family of the Slovak Republic, Slovakia, matej.belin@employment.gov.sk

This session focusses on research that explores, develops or tests data collection methods applied in empirical studies into vulnerable populations. These are populations like low literate people, people with cognitive limitations, (mental) health challenges, physical restraints like in hearing or speaking, and whose voices may be under- or misrepresented in research. They all have in common to be at greater risk of communicative, cognitive and/or motivational restraints or of being hard to study due to institutional distrust, power differences and the like. These limitations pose a threat to the quality of the data obtained and the resulting research outcomes.
In this session, we welcome theoretical and empirical contributions that address the use of structured or unstructured data collection for vulnerable populations, whether in quantitative surveys, qualitative field research, or mixed-methods research. The alternative/non-traditional data collection methods may be discussed or evaluated in relation to issues like cognitive and communicative processes, verbal and non-verbal behavior, power differences in research, sensitive topics and emotional burden, or other interactive aspects of data collection. In the same vein, contributions are welcomed that concentrate on the development of tools and instruments, like to aid recall or to map data visually, that aim to support data collection.

Hence, this session invites a broad range of papers dealing with innovative approaches to collect data from vulnerable populations.

Session Organizers:
Wander VAN DER VAART, University of Humanistic Studies, Netherlands, w.vandervaart@uvh.nl
Melissa NAVARRA, Ateneo de Manila University, Philippines, mnavarra@ateneo.edu

This session critically examines the expanding use of big data and data engineering in the study of social phenomena under conditions of rapid transformation and global turbulence. Contemporary challenges – including persistent inequalities, poverty, environmental crises, armed conflicts, and the spread of misinformation – have prompted a proliferation of data-driven projects, policy interventions, and technological innovations. With the increasing availability of large-scale datasets and advanced computational tools, data engineering is often positioned as a powerful means to assess social conditions, identify patterns, model causal mechanisms, and evaluate policy impacts.
This session interrogates the epistemological assumptions and methodological logics underpinning this “data-centric turn” in sociology. While recognizing the analytical strengths of big data techniques, it challenges the tendency to treat them as universally applicable or inherently superior modes of inquiry. Instead, it highlights the risks of abstraction, decontextualization, and the marginalization of lived experience in data-driven analyses.

We invite contributions that critically reflect on the selection, construction, and interpretation of data in relation to specific social contexts. In particular, we foreground the continued importance of small data, qualitative evidence, and subjective experiences in producing sociological knowledge. By encouraging dialogue between computational, quantitative, and interpretive approaches, this session advances a reflexive and pluralist methodological agenda for understanding complex social realities.

Session Organizer:
Ka Yi FUNG, Saint Francis University, Hong Kong, kyfung@sfu.edu.hk

Although emotions are increasingly recognized as integral to the research process (Berger, 2013; Bergman Blix & Wettergren, 2015; Boler, 2018), a significant gap remains between the ‘messiness’ of fieldwork and the production of formal academic knowledge (Keller & Pierce, 2024; Bloch, 2016). This session invites scholars to reflect on writing – during and after fieldwork – not merely as a reporting tool but as a primary site for managing, negotiating, and transforming emotional labor into knowledge. It addresses an observed tension between the unavoidable presence of emotions throughout the qualitative research process and the limited consideration of them within formal academic structures.
How can writing “behind the desk” function as a second life for emotional labor, where the “wound” opened by the encounter with the Other is processed into a reflexive resource (Grippo & Russo, 2026)? We encourage exploring how the act of writing can foster a “disciplined reflexivity” that integrates the researcher’s subjectivity (Hubbard et al., 2001; Keller & Pierce, 2024). We welcome contributions on how institutional expectations shape, discipline, and sometimes marginalise emotional experience in the production of knowledge; field diaries and journals as spaces for processing affective experience; methodological strategies that allow transforming emotional “perturbations” into analytical processes; negotiating the tension between analytical clarity and the ethical responsibility of remaining “touched” by the field; as well as other ways of integrating emotions into research as epistemic resources.

Our goal is to reclaim space for emotions while maintaining the rigor necessary to make social complexity comprehensible.

Session Organizers:
Dr. Inga GAIZAUSKAITE, Vytautas Magnus University, Lithuania, inga.gaizauskaite@lstc.lt
Concetta RUSSO, Guglielmo Marconi University, Italy, c.russo@unimarconi.it

Research on career boundaries has increasingly shifted its focus from the study of existing boundaries to the concept of boundaryless careers. In an increasingly globalized world, employees are more likely than ever to move across organizational, occupational, and geographical boundaries. Individuals may also transition between employment and non-employment, reflecting the growing fluidity of career trajectories. Consequently, the notion of boundarylessness has become a central theme in the sociology of careers.
However, boundarylessness itself may constitute a form of boundary. Access to boundaryless career opportunities is unevenly distributed across social categories such as age, gender, locality, social class, ethnicity, and nationality. As a result, the capacity to transcend traditional career boundaries is itself constrained by structural and social inequalities. These “boundaries of boundarylessness” may be powerful and enduring, limiting individuals’ ability to move across careers and life domains. While some of these boundaries evolve over time, others remain remarkably persistent.

This session invites papers that explore the boundaries and/or boundarylessness of women’s careers from these perspectives. We welcome both theoretical and empirical contributions, drawing on either qualitative or quantitative methodologies. Studies from diverse disciplinary and geographical contexts are encouraged.

Keywords: boundaries, boundarylessness, women’s careers, inequality, methodology

Session Organizers:
Fumiya ONAKA, Japan Women’s University, Japan, fonaka@fc.jwu.ac.jp
Mikyung PARK, Japan Women’s University, Republic of Korea, kyung0803@hotmail.com
Hoinam NHO, Japan Women’s University, Republic of Korea, hnho@fc.jwu.ac.jp

This methodological session explores the integration of quantitative approaches and creative methods in contemporary social research, addressing both their transformative potential and the epistemological, ethical and practical tensions they generate. While mixed-methods research often frames integration as a technical issue of sequencing, triangulation or complementarity, the encounter between surveys, administrative data, indicators statistical modelling, with visual methods, filmmaking, storytelling, arts-based practices, mapping, performance and narrative devices raises deeper questions. What counts as evidence when numbers, embodied experience, situated knowledge, imagination and aesthetic forms are brought into the same research design? How can different regimes of validity, reliability, interpretation and accountability enter into dialogue without one methodological logic being subordinated to another?

The session is particularly interested in contributions that examine different levels of integration, from research designs in which creative methods support data collection or dissemination, to studies in which they actively shape research questions, analytical categories, interpretation, impact evaluation and forms of public restitution. We especially welcome papers examining the role of creative techniques in producing, interpreting and legitimising knowledge across research designs that combine quantitative and creative approaches.

Contributions may address, among others:
visual, sensory, arts-based and performative methods;
filmmaking, photography, drawing, mapping and storytelling;
co-design, participatory workshops and collaborative interpretation;
citizen science, digital methods and creative data practices;
mixed-methods designs integrating surveys, indicators, modelling or administrative data with creative outputs.
The session foregrounds challenges of integration itself: participation, power asymmetries, authorship, representation, accessibility, analytical comparability, feasibility, resources, evaluation criteria and the sustainability of interdisciplinary infrastructures.

Session Organizers:
Alessandra DECATALDO, University of Milan Bicocca, Italy, alessandra.decataldo@unimib.it
Elena ANDREONI, University of Milan Bicocca, Italy, elena.andreoni@unimib.it
Federico PALEARDI, Università degli studi di Milano-Bicocca, Italy, federico.paleardi@unimib.it

This session addresses the question of how complex visual phenomena of the digital present can be analyzed systematically. While classical approaches to image analysis have traditionally focused on single images, contemporary digital media are characterized by networked, serial, and platform-specific visual forms. Images increasingly appear as elements of posts, feeds, iconic image clusters (Mueller, 2024), audiovisual sequences, visual vernaculars, and digitally mediated discourses. Meaning emerges relationally through seriality, referentiality, multimodal communication, and algorithmically shaped visibility (Bucher, 2018).
From a sociological perspective, these visual formations constitute central modes of contemporary social communication. As emphasized in visual sociology (Emmison, Smith, and Mayall, 2012), visual data provide insight into how actors construct meaning, negotiate identities, communicate experiences, perform belonging, and organize visibility in everyday life. They also offer access to processes of self-presentation, networked publics (boyd, 2014), collective memory, public discourse, and digital transformation.

The growing importance of such phenomena raises methodological challenges. If visual communication unfolds through image clusters, platform architectures, visual vernaculars, and audiovisual assemblages rather than isolated images, qualitative methods must account for the communicative structures through which meaning is produced, circulated, and transformed.

The session aims to discuss conceptual and methodological foundations for the qualitative analysis of visual data. Particular attention will be paid to single images, image clusters, images embedded in discursive interactions, visual vernaculars, and video and audiovisual data. We welcome contributions that introduce innovative methods, adapt established image-analytic approaches to emerging forms of digital data, or reflect on methodological challenges through empirically grounded case studies.

Session Organizers:
Anne SONNENMOSER, Technische Universität Chemnitz, Germany, anne.sonnenmoser@phil.tu-chemnitz.de
Michael R. MÜLLER, Chemnitz University of Technology, Germany, michael-r.mueller@phil.tu-chemnitz.de

The digital public sphere has mutated into a fragmented landscape where algorithmic architectures often accelerate ideological divides. To move beyond descriptive accounts of this phenomenon, contemporary sociology requires rigorous, scalable computational methodologies. Hosted by RC33 Logic and Methodology in Sociology, this session provides an international forum for cutting-edge empirical research that deploys Big Data analytics and Social Network Analysis (SNA) to detect, model, and map digital political polarization.
We invite innovative abstract proposals that leverage high-volume data mining and network metrics to dissect the structural properties of online conflict. The session is highly interested in methodological workflows capable of tracking electoral campaigns, identifying echo chambers, and mapping the attitudes or ideologies of online social network users. We welcome applied works using advanced quantitative and computational methods to study contemporary social processes, including social network analysis, community detection, semantic and discursive networks, computational text analysis, machine learning, artificial intelligence, and large language models (LLMs), among others.

By prioritizing robust computational frameworks, this session aims to foster deep interdisciplinary collaboration between data science and political sociology. Ultimately, our goal is to enhance the discipline’s analytical and explanatory capacity to understand how interactional structures shape modern democratic crises.

Session Organizer:
Juan Antonio GUEVARA, Complutense University of Madrid, Spain, Spain, juanguev@ucm.es

The unprecedented volume of disinformation, misinformation, and populist rhetoric circulating online raises the question about the necessasity of a methodological paradigm shift in the social sciences. To accurately decode these post-truth dynamics, large-scale digital data analysis maybe the method of choice. Hosted by RC33 Logic and Methodology in Sociology, this session focuses on the application of computational text analysis and Big Data methodologies to uncover the narrative and cognitive dimensions of contemporary political manipulation.

While other analytical approaches might map network topologies, this session prioritizes the massive extraction and semantic analysis of digital content. We aim to understand how distorted messages are constructed, how populist emotional grammar operates at scale, and how these phenomena alter public discussion processes on controversial issues. We invite applied
empirical works that employ Big Data techniques to trace the linguistic lifecycle of fake news across different digital platforms.

Specifically, we welcome abstract proposals utilizing Natural Language Processing (NLP), structural topic modeling, automated sentiment and toxicity analysis, and machine learning classifiers designed to detect deceptive content. By integrating these advanced computational methods, the session aims to foster interdisciplinary collaboration and significantly improve our analytical and explanatory capacity. Our ultimate goal is to equip sociologists with the robust, data-driven tools necessary to evaluate the discursive impact of post-truth dynamics on modern democracies.

Session Organizer:
Belen CASAS MAS, Universidad Complutense de Madrid, Spain, bcasas@ucm.es

Dialectics is not a method, but a critical way of being with contradiction. This session repositions dialectics not as a fixed framework, but as a dynamic, context-sensitive practice for qualitative research. In an era of escalating social contradictions – climate crisis, digital capitalism, racial and gendered violence – dialectical thinking offers a vital, generative force beyond descriptive accounts or theoretical templates.
This session seeks to explore how dialectical thinking is being reinterpreted and applied in qualitative research and how dialectics can serve as a bridge between situated empirical inquiry and broader sociological theorizing. We focus on three interconnected dimensions. First, dialectics as epistemic practice: the use of contradiction and tension as generative forces in data analysis and theory-building. Second, dialectics as reflexive praxis: the critical self-awareness required when researchers engage with power, positionality, and knowledge production across cultural and political divides. Third, dialectics as methodology: the dynamic interplay between lived experience, theoretical concepts, and empathetic understanding – where the universal emerges from the particular, across diverse social realities.

This session aims to foster a transnational dialogue on dialectics as a methodological commitment – one that remains open to difference, contradiction, and the unpredictable emergence of new social forms. We invite contributions that engage dialectical thinking as a generative force in data analysis, theory-building, and ethical inquiry – without reducing it to techniques or templates. We do not seek to standardize dialectics, but to open it to multiple ways of being, doing, and knowing – across cultures, languages, and traditions.

Session Organizers:
Jasmin SCHREYER, FAU Erlangen-Nuernberg, Germany, jasmin.schreyer@fau.de
Dennis Eckhardt ECKHARDT, Goethe University Frankfurt/Main, Germany, dennis.eckhardt@fau.de
Peter SCHULZ, Friedrich-Schiller-Universität Jena, Germany, schulz.peter@uni-jena.de

The XXI ISA World Congress highlights how AI is reshaping “the nature of knowledge and intellectual work”. This session places that transformation at the center of RC33’s agenda by asking a fundamental methodological question: what happens to sociological inquiry when key stages of knowledge production are increasingly delegated to computational systems?
The challenge posed by AI is not merely technical but epistemological and political. Machine learning models classify survey responses, large language models assist qualitative coding, generative systems synthesize documents, and algorithmic infrastructures increasingly mediate the production of evidence itself. Yet the inferential chain connecting computational outputs to sociological claims often remains opaque. While current debates oscillate between enthusiasm for new analytical capacities and concerns about ethical risks, less attention has been devoted to understanding how algorithmic outputs become evidence and how evidence becomes sociological knowledge.

This question is relevant in a context where knowledge production is shaped by inequalities in access to data, computational resources, proprietary models, and research infrastructures. AI is not only transforming methods; it is also redistributing epistemic authority and redefining who can produce, validate, and govern knowledge.

Three dimensions structure the session: technical validation (on quality, robustness, and reproducibility of AI-generated outputs); interpretive construction (on translation of algorithmic patterns into sociological meaning); epistemic governance (on accountability, transparency, and power relations in automated research workflows).

Empirical studies, theoretical frameworks, methodological innovations, and critical reflections in light of comparative, global, and Global South perspectives on AI, evidence, and research governance are especially welcome.

Session Organizers:
Francesco MAZZEO RINALDI fmazzeo@unict.it
Gabriella PUNZIANO, University of Naples Federico II, Italy, GABRIELLA.PUNZIANO@UNINA.IT

Contemporary societies increasingly operate under conditions of polycrisis, such as pandemics, armed conflicts, forced migration, and climate-related disasters. Such crises do not merely constitute research topics; they also transform the very conditions under which social research is designed, conducted, and evaluated. Understanding these transformations has become an important methodological and ethical challenge.
Therefore, we invite contributions examining how qualitative researchers navigate the challenges of conducting research during and after social crises. We are particularly interested in how crises reshape access to the field, researcher-participant relationships, research temporalities, and experiences of vulnerability, responsibility, care, and risk.

A central concern of the session is the relationship between methodological choices and ethical responsibilities in crisis settings, where established research procedures often become difficult to sustain. We also seek to explore whether research conducted in crises follows the logic of rapid-response disaster research or that of longer-term “slow disaster” inquiry, and how these different temporalities shape research practices.

Key questions include: How do researchers adapt their methodological and ethical practices under changing conditions? What ethical dilemmas emerge when studying populations affected by crisis? What forms of reflexivity, resilience, and methodological innovation arise in crisis contexts? To what extent do crisis-driven adaptations persist beyond the crisis itself?

We welcome empirical, methodological, theoretical, and reflexive contributions, including studies that place researchers’ own experiences, decisions, and practices at the centre of analysis. Cross-contextual perspectives are encouraged.

The session aims to enrich methodological debate on conducting ethical and reflexive qualitative research in an increasingly uncertain world.

Session Organizers:
Sylwia MECFAL, University of Lodz, Poland, sylwia.mecfal@uni.lodz.pl
Beata BIELSKA, Nicolaus Copernicus University in Torun, Poland, bielska.beata@umk.pl
Adrianna SURMIAK, University of Warsaw, Poland, a.surmiak@uw.edu.pl

Pioneering decisions require a solid understanding of public opinions and acceptance of future-oriented technologies, innovative processes, and emerging social issues. However, measuring opinions towards complex, unfamiliar, or future-oriented topics poses significant methodological challenges that undermine measurement validity in particular (e.g., by eliciting pseudo-opinions or item nonresponse).
This session focuses on the intersection of general survey methodology, opinion, and acceptance research. We explore how experimental designs, innovative data collection, and qualitative approaches can improve the measurement and representation quality of preferences for novel or abstract acceptance objects.

We welcome progressive theoretical, conceptual, and empirical contributions that advance the field of acceptance research. Topics include, among others:

Conceptualization & Operationalization: Advancements in theoretical definitions of acceptance and the operationalization of valid survey instruments for capturing acceptance.
Experimental Survey Designs: Utilizing survey experiments, discrete choice experiments, and factorial surveys to validly measure preferences and trade-offs.
Qualitative & Mixed-Methods Approaches: Using qualitative methods (e.g., cognitive interviewing, focus groups) or mixed-methods to explore the formation of opinions on unfamiliar topics and refine quantitative instruments or for envisioning future acceptance.
Tackling Pseudo-Opinions & Non-Response: Strategies to reduce non-opinions and cognitive overload when respondents lack pre-existing knowledge.
Leveraging Paradata & Survey Quality: Using paradata (e.g., response times) or mixed-mode designs to monitor respondent motivation and minimize processing errors or survey bias.
By linking advanced survey tools and qualitative insights with the demands of opinion and acceptance research, this session fosters a rigorous discussion on validly measuring preferences for the world of tomorrow.

Session Organizers:
Hawal SHAMON, Forschungszentrum Jülich, Germany, h.shamon@fz-juelich.de
Andrea WINGEN, Forschungszentrum Jülich, Germany, a.wingen@fz-juelich.de
Simon BRAUNER, Forschungszentrum Jülich, Germany, s.brauner@fz-juelich.de

Qualitative research generates vast amounts of textual, visual, and audio data that require labour-intensive abstraction. Using technology like Generative Artificial Intelligence (GenAI) to streamline and accelerate data collection, analysis, and reporting procedures is appealing. However, inherently qualitative research relies on a researcher’s social interaction with research participants, attention to the complexities of social contexts and research fields, as well as in-depth analytical engagement with the data. It is therefore unsurprising that the use of GenAI in qualitative research is debated among researchers. Some advocate for incorporating Large Language Models (LLMs) in the research process, while others warn of the loss of human understanding when artificial intelligence is used to construct meaning from narrative data or even to conduct qualitative research interviews without a human interviewer. A third approach suggests moving beyond these binary positions to adopt reflexive and responsible use of this technology when conducting qualitative research.
The session invites researchers to critically contribute to this debate, reflecting on potential uses or, conversely, the rejection of GenAI use in qualitative research. We encourage contributions that cover different stages of the qualitative research process and highlight epistemological, methodological, analytical, or other tensions. The session aims to facilitate a discussion on how to navigate the seemingly unstoppable presence of GenAI in qualitative research, addressing its usefulness (or not) and offering suggestions for ensuring quality, upholding the value of transparency, and adhering to ethical issues.

Session Organizers:
Dr. Inga GAIZAUSKAITE, Lithuanian Centre for Social Sciences, Lithuania, inga.gaizauskaite@lstc.lt
Claire WAGNER, University of Pretoria, South Africa, claire.wagner@up.ac.za

For more than two decades, sociologists such as David Franks, Jonathan Turner, Bernard Lahire, and Warren D. TenHouten have urged sociology to revisit neuroscience findings. This need is especially pressing in the current context of neuroscience and biomedical research, which has found a new niche for analyzing social phenomena. This interest stems, in part, from the usefulness of certain physiological indicators and advances in portable, less expensive technologies, which now make it possible to analyze, in real time and under ecologically valid conditions, measures of heart rate variability (ECG), electrodermal activity (EDA), and brain frequency (EEG) contrasts between participants. Heart rate analysis can reveal synchronization among participants in collaborative and emotionally relevant activities; electrodermal activity can reveal stressful interactions; and synchronized participants’ brain frequencies can show increased engagement in social activities. Even so, these advances pose significant challenges for the social sciences, raising questions about how to interpret these findings.
Papers for this session should therefore address one or more of the following questions:

How are individuals’ physiological reactions or movements interpreted, and how can sociology explain this without falling into biological reductionism?
How can sociology use biological variables to define categories for studying situated agency and social relations?
What interdisciplinary methodology for research on the body and emotions emerges from the intersection of sociology, psychology, and neuroscience?
How can data from other disciplines be translated to understand social relations, power, and stratification?
Keywords: Interdisciplinary research; mixed methodologies; body; emotion; EDA; ECG; EEG.

Session Organizer:
Geronimo MEDRANO LOERA, Universidad Autónoma Metropolitana, Mexico, sociologogmedrano@gmail.com

Anchored in the Congress theme, “Global Sociology in Turbulent Times,” this session examines what makes questions “sensitive,” why sensitivity shifts across cultures and over time, and how researchers can study sensitive topics with qualitative, quantitative, and mixed methods. We conceptualize sensitivity as arising from stigma, moral judgment, legal or material risk, identity threat, and power relations – factors shaped by cultural norms, language, institutions, platform contexts, and historical change.
We invite papers that:

Theorize sensitivity across domains (e.g., sexuality, politics, health, work, migration, crime) and show how global and local turbulence reclassifies topics as sensitive or safe.
Diagnose bias mechanisms and error modes – such as social desirability, fear and reputational risk, motivated underreporting, recall suppression, and mode/platform effects – and identify their contextual triggers (including censorship, surveillance, and platform norms).
Offer practically usable strategies for survey methods, qualitative interviewing, or other methods, including non-reactive approaches.
Reflect on ethics, consent, trauma- and risk-aware practices, and cross-jurisdictional data protection under heightened risk.
Address cross-cultural comparability, measurement invariance, subgroup differences, and equity in access and exposure, including work from the Global South and hard-to-reach or high-risk populations.

Session Organizer:
Martin WEICHBOLD, University of Salzburg, Austria, martin.weichbold@sbg.ac.at

Artificial intelligence (AI) is increasingly being integrated into sociological research. AI is used, for example, to support literature reviews, formulate research questions and hypotheses, develop and test experimental stimuli and survey instruments, and process, analyze, and interpret research data. While these advances create novel opportunities for sociological knowledge production, they also raise fundamental questions about scientific integrity, transparency, accountability, validity, and reproducibility.
A key challenge is how established standards of good scientific practice can be maintained and adjusted under conditions of AI-assisted sociological research. How does the use of AI affect the quality and credibility of empirical findings? How can AI-assisted research processes be documented and made transparent and traceable? What new challenges emerge for scientific responsibility? And what methodological implications follow for the production and evaluation of sociological knowledge?

This session invites theoretical, methodological, and empirical contributions that examine the opportunities, limitations, and risks of AI-assisted sociological research. We particularly welcome contributions that address the following topics (but are not limited to them):

1) Documentation, transparency, and reproducibility of AI-assisted research outcomes.
2) Development of research questions and hypotheses, as well as experimental stimuli, survey instruments, and study designs.
3) Data collection and generation, as well as qualitative and quantitative data analysis.
4) Evaluation of AI-assisted research methods (e.g., chatbot-assisted interviewing and data classification).
5) Quality assurance (e.g., bias, hallucinations, and spillover effects).
6) Scientific integrity and research ethics in the context of AI (e.g., within peer review and publication processes).
7) AI in open science, data management, and sociological training.

Session Organizers:
Joshua CLAASSEN, DZHW, Leibniz University Hannover, Germany; Leibniz University Hannover, Germany, claassen@dzhw.eu
Hannah SCHWAERZEL, Technical University of Darmstadt, Germany, schwaerzel@ifs.tu-darmstadt.de
Jan Karem HÖHNE, German Centre for Higher Education Research and Science Studies (DZHW), Germany; Leibniz University Hannover, Germany, hoehne@dzhw.eu

Generative Artificial Intelligence (GenAI) has changed and continues to change how research in the social sciences is conducted and published. In empirical research, GenAI has affected established practices from hypotheses generation to data collection, data analysis, model building, and interpretation. Whereas some changes seem minor, others, such as the use of GenAI in inductive coding, pose unresolved questions of reliability and validity. For writing, text-generating tools such as ChatGPT have raised wide-ranging questions regarding originality, authorship, and slop. GenAI is affecting also the review and editing process, for example, when “AI Assistants” help journal editors and publishers with copyediting, index creation, and more.
Many developments seem rapid, with researchers struggling to keep up. Hopes, promises and opportunities go hand in hand with uncertainties, risks, and fears. Many challenges continue to remain unresolved while the stream of new challenges seems to grow. This session aims to address the challenges related to GenAI in the social science research process. What innovations of GenAI uses for research are on the horizon? Is our research and writing still our “own”? How can or should we (re-)organize our research, writing, editing, and teaching practices? How does the rise of GenAI change the scientific profession, its self-image, and standing in society? What should be done about alienation of intellectual work from the scientific process? What kind of new policies and collective interventions do we need? What can academic organizations do to meet the challenges? What methodological innovations shall we embrace, avoid, and seek to develop?

Session Organizers:
Dr. Inga GAIZAUSKAITE, Lithuanian Centre for Social Sciences, Lithuania, inga.gaizauskaite@lstc.lt
Michael GROTHE-HAMMER, Norwegian University of Science and Technology, Norway, michael.grothe-hammer@ntnu.no
Dirk MICHEL-SCHERTGES, Aarhus University, Denmark, dimi@edu.au.dk
Markus S. SCHULZ, FLACSO, Mexico, markus@markus-s-schulz.net

Data donations are increasingly becoming part of the sociological methodological toolkit. As a form of participatory research involving individuals willing to contribute their personal data for social research purposes, donated data has been integrated with a variety of qualitative, quantitative, and computational approaches to study phenomena ranging from cultural consumption and algorithmic curation to media use.
While this has been welcomed as a practice that foregrounds participant involvement and fosters awareness between participants, concerns have also been raised about whether and how data donations can be approached ethically, particularly regarding informed consent, data sensitivity, and the asymmetries of knowledge between researchers and participants.

Methodologically, data donations have been combined with surveys, in-depth interviews, and walkthrough methods in a variety of triangulation designs. Yet how these combinations are best calibrated – and what each method distinctively contributes to the analytical picture – remains an open question. Epistemologically, the intermediation of digital infrastructures, platform affordances, social backgrounds, and individual factors introduces constraints on what donated data can and cannot tell us, which remain undertheorized.

This session welcomes interdisciplinary papers drawing on mixed-method approaches. We specifically welcome contributions addressing:

Empirical examples of data donation studies.
Epistemological reflections on the limits and potentials of donated data.
Infrastructural and technical considerations in designing data donation pipelines.
Action-research approaches where the donation process itself becomes an educational intervention.
The integration of data donation with ethnographic or digital ethnographic approaches.
How participants make sense of their own data when confronted with it during research encounters.

Session Organizers:
Ilir RAMA, University of Milan, Italy, ilir.rama@unimi.it
Giulia GIORGI, University of Milan, Italy, giulia.giorgi1@unimi.it