{"id":1346,"date":"2026-06-19T13:12:14","date_gmt":"2026-06-19T13:12:14","guid":{"rendered":"https:\/\/www.ahmadinia.fi\/?p=1346"},"modified":"2026-06-19T13:12:14","modified_gmt":"2026-06-19T13:12:14","slug":"reflections-from-ecsr-2026-dublin-social-stratification-research-in-the-age-of-big-data","status":"publish","type":"post","link":"https:\/\/www.ahmadinia.fi\/index.php\/2026\/06\/19\/reflections-from-ecsr-2026-dublin-social-stratification-research-in-the-age-of-big-data\/","title":{"rendered":"Reflections from ECSR 2026 Dublin: Social Stratification Research in the Age of Big Data"},"content":{"rendered":"\n

Attending the ECSR 2026 Conference in Dublin<\/strong> was an important opportunity for me to reflect on current debates in social stratification research. One of the strongest moments of the conference was the keynote lecture by Professor Moris Triventi<\/strong> from the University of Milan<\/strong>, titled \u201cSocial Stratification Research in the Age of Big Data: Challenges and Opportunities Ahead”.<\/strong><\/p>\n\n\n\n

The keynote was not simply about big data. It was about what big data can do for social stratification research when it is used carefully, theoretically, and cumulatively. Professor Triventi\u2019s central message was clear: big data and big models are not enough by themselves<\/strong>. They become valuable only when they are connected to strong theory, shared questions, careful measurement, transparent inference, and a serious scientific programme.<\/p>\n\n\n\n

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Before moving into the main argument, Professor Triventi used a background slide with a comic-style visual that placed the talk in a more personal and reflective frame. I found this opening effective because it reminded the audience that the keynote was not only about methods or data. It was also about how a research field thinks about its past, present, and future.<\/p>\n\n\n\n

The slide visually connected Dublin, Milan, and the work of social stratification research. It created a lighter moment at the beginning of the keynote, but it also introduced a serious question: how should scholars reflect on what the field has already achieved while preparing for the methodological and substantive challenges ahead?<\/p>\n\n\n\n

This background helped set the tone for the rest of the lecture. The keynote was forward-looking, but it was not detached from the history of the field. Professor Triventi\u2019s argument was that social stratification research should engage with new data and methods without losing the theoretical, empirical, and cumulative traditions that have made the field strong.<\/p>\n\n\n\n

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After the background slide, Professor Triventi presented the roadmap for the keynote. This outline was useful because it showed that the talk was not organised around big data alone. Instead, it moved through a broader intellectual sequence: what the field has already learned, what has changed, what risks accompany the expansion of data and methods, where the field should go, and why these questions matter.<\/p>\n\n\n\n

I found this structure helpful because it framed the keynote as both a review and an agenda-setting lecture. The talk first returned to the foundations of social stratification research, then considered how new data and methods are changing the field, and finally asked how stratification research can remain theoretically grounded, empirically rigorous, and relevant to public problems.<\/p>\n\n\n\n

The roadmap also made clear that Professor Triventi was not arguing for methodological novelty for its own sake. The keynote was about how new tools can strengthen the field only when they are connected to shared questions, cumulative knowledge, and better explanations of inequality.<\/p>\n\n\n\n

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Before moving into the main argument, Professor Triventi also clarified what the keynote would not cover. He explicitly placed artificial intelligence, data privacy, ethics, institutional development, and relations with policymakers outside the main scope of the talk.<\/p>\n\n\n\n

I found this clarification useful because it narrowed the lecture in a disciplined way. In a keynote about big data, it would have been easy to move into broad debates about AI, governance, privacy, or institutional reform. Instead, Professor Triventi focused on a more specific question: how can new data and methods strengthen social stratification research as a scientific field?<\/p>\n\n\n\n

This helped the audience understand that the talk was not about every possible implication of big data. It was about the internal scientific programme of stratification research: its questions, concepts, methods, risks, and opportunities.<\/p>\n\n\n\n

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Big data is an opportunity, but not a solution by itself<\/h2>\n\n\n\n

The talk then placed social stratification research within the wider transformation of the social sciences. Researchers now have access to larger datasets, administrative records, longitudinal data, social media data, mobile phone data, satellite data, and increasingly powerful computational methods. These tools allow researchers to study inequalities in ways that were not possible before.<\/p>\n\n\n\n

Professor Triventi showed examples of studies using millions of observations and large-scale computational approaches. These examples demonstrated the scale of contemporary empirical research. However, he did not present big data as a magic solution. Instead, he warned that larger datasets and more complex models do not automatically produce better social science.<\/p>\n\n\n\n

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This was one of the most important points of the keynote. The value of big data depends on the questions we ask with it. Without clear concepts and theory, big data can produce impressive-looking results without strong sociological meaning.<\/p>\n\n\n\n

Two contrasting positions in the field<\/h2>\n\n\n\n

A particularly useful part of the keynote was the contrast between two positions.<\/p>\n\n\n\n

On one side is the computational enthusiast<\/strong>. This position emphasises data mining, algorithms, prediction, scalability, pattern detection, and computational performance. It brings important strengths, especially when researchers need to detect complex patterns or work with large and heterogeneous datasets. But it also carries risks: weak theoretical grounding, unclear constructs, and black-box explanations.<\/p>\n\n\n\n

On the other side is the traditional stratification researcher<\/strong>. This position emphasises theory, hypotheses, mechanisms, causal interpretation, social meaning, and cumulative knowledge. It provides the conceptual backbone of the field. But it can also become too restrictive if it ignores complexity, heterogeneity, and the opportunities offered by new data and methods.<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

The keynote did not argue that one position should replace the other. Instead, it called for integration. Computational methods can strengthen social stratification research, but only when they are embedded in theory, clear concepts, and disciplined research designs.<\/p>\n\n\n\n

In this sense, the keynote offered a constructive middle position. It did not reject computational methods, nor did it abandon traditional sociological explanation. It asked how the two can be combined in a more rigorous research programme.<\/p>\n\n\n\n

What social stratification research studies<\/h2>\n\n\n\n

Professor Triventi defined social stratification research as the study of how advantages and disadvantages are structured, reproduced, and transmitted across individuals, groups, and generations through systems of unequal life chances.<\/p>\n\n\n\n

This definition is useful because it reminds us that stratification is not only about income, education, class, or occupation separately. It is about how different forms of advantage and disadvantage are connected across the life course and across generations.<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

The traditional focus of the field has often been the relation between social origin, education, and destination. This classic framework remains important because family background continues to shape educational opportunities, and education continues to shape later life chances. But the keynote also showed that the field has expanded far beyond this model.<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

Professor Triventi\u2019s slide on the expansion of the field made this shift very clear. The traditional core of stratification research remains important: parents\u2019 social class shapes education and academic performance, and these in turn shape later social class. But the field now studies a much wider set of origins and outcomes.<\/p>\n\n\n\n

On the origin side, stratification research increasingly examines family education, income, wealth, gender, ethnic and migration background, and geographic origin. On the outcome side, it now extends beyond social class to labour-market outcomes, family trajectories, health, consumption, attitudes, and political ideology and behaviour.<\/p>\n\n\n\n

I found this slide useful because it showed both continuity and change. The field has not abandoned its classic questions about social origin, education, and destination. Instead, it has expanded those questions into a broader research agenda about how multiple forms of advantage and disadvantage are connected across the life course.<\/p>\n\n\n\n

The expansion of the field is important. Social stratification research now examines gender, migration background, ethnicity, geographic origin, health, consumption, attitudes, political behaviour, and many other outcomes. This broader scope creates exciting possibilities, but it also requires stronger conceptual organisation. Without shared questions and comparable measures, an expanding field can easily become fragmented.<\/p>\n\n\n\n

What the field has already learned<\/h2>\n\n\n\n

One of the strengths of the keynote was that it did not treat social stratification research as a field starting from zero. Professor Triventi reminded the audience that the field has already accumulated important empirical knowledge.<\/p>\n\n\n\n

We know that occupational hierarchies and mobility patterns show considerable stability across countries and over time. We know that education remains a major channel of both upward mobility and intergenerational reproduction. We know that inequalities persist beyond education, because social origin continues to shape occupational rewards even after educational attainment is considered. We also know that educational institutions matter, including tracking systems, school structures, and institutional arrangements.<\/p>\n\n\n\n

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This part of the keynote was important because it showed the value of cumulative research. The field has already built a strong foundation. The challenge now is to connect new data and methods to that foundation rather than letting the field fragment into disconnected studies.<\/p>\n\n\n\n

The message was not that previous stratification research is obsolete. The opposite was true. Professor Triventi\u2019s argument was that the field should use new data and methods to extend and refine existing knowledge, not to forget it.<\/p>\n\n\n\n

More than \u201cmore data\u201d<\/h2>\n\n\n\n

One of the strongest messages of the keynote was that the real transformation is not simply about having more data. What matters is what new data allow researchers to observe.<\/p>\n\n\n\n

New data can offer temporal depth<\/strong>, allowing researchers to study trajectories, transitions, and cumulative processes over time. They can offer contextual precision<\/strong>, allowing researchers to examine local settings, schools, regions, and institutions in more detail. They can offer relational embeddedness<\/strong>, linking individuals to families, peers, teachers, institutions, and networks. They can also offer analytical flexibility<\/strong>, allowing researchers to combine description, causal inference, and prediction.<\/p>\n\n\n\n

This point is essential. Big data should not only make old models larger. It should help researchers ask better questions about inequality.<\/p>\n\n\n\n

Professor Triventi also showed that improved data and methods allow researchers to study inequalities that are distributional, intersecting, dynamic, and spatially embedded. This means moving beyond average outcomes, taking seriously how multiple social characteristics combine, following inequalities across the life course, and recognising that local contexts shape opportunity.<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

For me, this was one of the most valuable parts of the keynote. It showed that new methods are not just technical instruments. They can change the kinds of sociological questions we are able to ask. They allow us to see inequality not only as a static gap but also as a dynamic, contextual, and relational process.<\/p>\n\n\n\n

Heterogeneous data infrastructures for inequality research<\/h2>\n\n\n\n

Professor Triventi also discussed different types of data infrastructures that can enrich inequality research. These included multi-actor data, multilevel data, longitudinal data, administrative data, text data, social media data, mobile phone data, and satellite data.<\/p>\n\n\n\n

Each type of data offers something different. Multi-actor data can connect students, parents, teachers, peers, and institutions. Multilevel data can locate individuals within schools, neighbourhoods, regions, and countries. Longitudinal data can follow life-course trajectories. Administrative data can provide broad coverage from public and institutional systems. Text and social media data can capture communication, attitudes, and digital traces.<\/p>\n\n\n\n

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At the same time, no data source is perfect. Survey data often contain rich concepts, but they may face declining response rates. Social media data can be large-scale and timely, but they are affected by selective participation and platform bias. Administrative data can be broad and complete, but they may include fewer theoretically meaningful variables.<\/p>\n\n\n\n

This is why data triangulation matters. Stronger research often comes from combining different sources, not from assuming that one source can answer every question.<\/p>\n\n\n\n

The risks of an expanding field<\/h2>\n\n\n\n

The keynote also identified risks that come with the expansion of social stratification research. One risk is fragmentation. As the field expands across topics, methods, and data sources, researchers may lose shared questions and common concepts.<\/p>\n\n\n\n

Another risk is data quality and representativeness. Large datasets are not automatically good datasets. They may be incomplete, biased, or poorly aligned with the concepts researchers want to study.<\/p>\n\n\n\n

A third risk is data-driven research replacing question-driven research. When research begins from available data rather than theoretically meaningful questions, the agenda can become shaped by convenience.<\/p>\n\n\n\n

A fourth risk is description without explanation. Description is necessary, but it is not enough. Researchers also need theory and mechanisms to explain why inequalities emerge and how they might be reduced.<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

The slide on the challenges of an expanding field brought these risks together clearly. Professor Triventi identified four dangers that come with new data and new methods: fragmentation of the field, problems of data quality and representativeness, data-driven rather than question-driven research, and description without explanation.<\/p>\n\n\n\n

What I found especially important was that the slide also pointed toward possible responses. Fragmentation requires shared questions. Data quality problems require triangulation. Data-driven research requires theory-driven designs. Description without explanation requires integrated analytical tools that connect micro and macro mechanisms.<\/p>\n\n\n\n

This framing made the keynote more interesting for me. The problem is not that the field is expanding. Expansion is necessary and productive. The problem is that expansion can weaken cumulative knowledge if it is not organised by common questions, robust concepts, and explanatory aims.<\/p>\n\n\n\n

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After discussing the risks of an expanding field, Professor Triventi presented a broader framework for developing a rigorous and cumulative social stratification field. The slide organised the agenda around six pillars: theory; data triangulation, measurement, integrated analytical tools, evidence synthesis and reproducibility; and policy relevance.<\/p>\n\n\n\n

I found this slide especially important because it connected many parts of the keynote into one integrated research programme. The point was not simply that the field needs better data or more advanced methods. It needs a structure that links theory, measurement, evidence, explanation, prediction, and policy relevance.<\/p>\n\n\n\n

The six pillars also clarified how cumulative knowledge can be built. Theory, data triangulation, and measurement provide the foundations. Integrated analytical tools then help generate stronger evidence through description, causal inference, and prediction. Evidence synthesis and reproducibility allow findings to accumulate and be validated. Policy relevance connects scientific knowledge back to social problems and public interventions.<\/p>\n\n\n\n

For me, this was one of the clearest summaries of the keynote\u2019s main argument: social stratification research can benefit from big data, but only if these tools are embedded in a coherent scientific programme.<\/p>\n\n\n\n

Opportunity 1: shared questions<\/h2>\n\n\n\n

One of Professor Triventi\u2019s central proposals was that social stratification research should rebuild cumulative knowledge around shared questions. These questions can help organise the field across different topics, datasets, and methods.<\/p>\n\n\n\n

The core questions are ‘Who gets what?’ Through which mechanisms? Under which conditions? With what intergenerational consequences? And what can reduce inequality?<\/p>\n\n\n\n

These questions are simple, but they are powerful. They can be applied to education, health, labour markets, migration, class, gender, and many other areas. They also help connect descriptive, explanatory, and policy-orientated research.<\/p>\n\n\n\n

For example, in education, we might ask why children from advantaged families achieve more. In health, we might ask why disadvantaged groups have worse health and shorter lives. The specific mechanisms differ, but the structure of inquiry remains comparable.<\/p>\n\n\n\n

For me, this was one of the most useful parts of the keynote. A field becomes cumulative not only by producing more studies but also by organising studies around questions that can speak to each other.<\/p>\n\n\n\n

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Opportunity 2: theory and mechanisms<\/h2>\n\n\n\n

One of the most useful sections of the keynote focused on theory. Professor Triventi argued that empirical research should continue to be embedded in solid theoretical work.<\/p>\n\n\n\n

Good theory should go beyond common sense. It should challenge taken-for-granted assumptions and reveal what is not obvious. It should avoid sterile overcomplication. It should specify mechanisms, generate testable propositions, clarify empirical implications, and specify scope conditions.<\/p>\n\n\n\n

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This was a powerful reminder that theory is not decoration. Theory should guide the whole research design. It should shape the research question, the concepts, the measurement strategy, the interpretation of results, and the limits of the claim.<\/p>\n\n\n\n

Good theory does not just explain the past. It guides inquiry, structures evidence, and helps findings travel beyond one case.<\/p>\n\n\n\n

Opportunity 3: data triangulation<\/h2>\n\n\n\n

Professor Triventi also emphasised data triangulation as a major opportunity for the field. This point became especially clear in the slide comparing survey data, social media data, and administrative data.<\/p>\n\n\n\n

Survey data are strong because they contain rich concepts and carefully designed questions, but they may suffer from declining response rates and limited territorial detail. Social media data are large-scale and timely, but they raise problems of selective participation and platform bias. Administrative data often provide broad coverage and completeness, but they may contain fewer theoretically meaningful variables.<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

The conclusion was not that one type of data should replace the others. Stronger research comes from combining data sources carefully. Triangulation matters because different data sources can compensate for each other\u2019s weaknesses and provide a fuller picture of inequality.<\/p>\n\n\n\n

This was a useful corrective to simplistic claims about big data. A large administrative dataset may offer coverage but not always meaning. A survey may offer meaning, but not always scale. Social media data may offer timeliness, but not always representativeness. The strongest research designs often combine these strengths while being transparent about their limitations.<\/p>\n\n\n\n

Opportunity 4: synthesis and reproducibility<\/h2>\n\n\n\n

Another opportunity Professor Triventi emphasised was synthesis and reproducibility. This part of the keynote connected strongly with the idea of cumulative knowledge. More data become more useful only when results are comparable, reproducible, replicable, and synthesizable.<\/p>\n\n\n\n

The slide identified four elements: shared indicators, reproducibility, replicability, and synthesis. Shared indicators allow researchers to compare concepts and measures across studies. Reproducibility means that the same data and code should lead to the same result. Replicability means that similar findings should appear in new data or settings when the underlying relationship is robust. Synthesis means building systematic reviews and meta-analyses that identify robust patterns, boundary conditions, and research gaps.<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

For me, this was one of the most important methodological messages of the keynote. A field does not become cumulative simply because it produces more studies. It becomes cumulative when studies can speak to one another. That requires comparable concepts, transparent workflows, shared measures, and a willingness to build knowledge beyond individual papers.<\/p>\n\n\n\n

Opportunity 5: Description, causal inference, and prediction should work together<\/h2>\n\n\n\n

Another important argument in the keynote was that description, causal inference, and prediction should not be treated as competing approaches. They are three complementary analytical tasks.<\/p>\n\n\n\n

Description remains indispensable because it forces researchers to document patterns carefully. It helps identify gradients, map variation, document new forms of inequality, and locate where disparities are greatest.<\/p>\n\n\n\n

Causal inference disciplines explanation by asking what would happen under alternative conditions. It forces researchers to specify the causal question, the estimand, the research design, and the assumptions.<\/p>\n\n\n\n

Prediction disciplines explanation by asking whether our explanations generate expectations beyond the observed case. It helps distinguish signals from overfitting and well-grounded explanations from post-hoc rationalisation.<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

Professor Triventi\u2019s slide on prediction made this point more concrete. Prediction was not presented as a replacement for explanation. Instead, it was presented as a tool for measurement, diagnosis, and theory-testing.<\/p>\n\n\n\n

Prediction can help researchers measure structured disadvantage by identifying who is most exposed to risks or lower chances. It can also help detect heterogeneity by showing whether predictive patterns differ across groups or contexts. It can test generalisation by asking whether a model travels across countries, cohorts, or settings. Finally, it can compare mechanisms by assessing the predictive power of models based on different theoretical explanations.<\/p>\n\n\n\n

I found this framing useful because it avoids a false opposition between prediction and explanation. Used carefully, prediction can discipline explanation. It asks whether our theoretical claims produce expectations that hold beyond the original case.<\/p>\n\n\n\n

This integrated view was one of my main takeaways. The description tells us where inequality is and how large it is. Causal inference asks why it emerges and what might change it. Prediction asks whether an explanation travels beyond the original case. Together, these tasks can produce stronger and more credible inequality research.<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

This integrated view was one of my main takeaways. The description tells us where inequality is and how large it is. Causal inference asks why it emerges and what might change it. Prediction asks whether an explanation travels beyond the original case. Together, these tasks can produce stronger and more credible inequality research.<\/p>\n\n\n\n

Moving beyond internal validity alone<\/h2>\n\n\n\n

A particularly important methodological point concerned causal inference. Professor Triventi argued that improved causal inference should go beyond internal validity alone. It should also pay attention to construct validity, external validity, treatment prevalence, and selection.<\/p>\n\n\n\n

Construct validity asks what researchers are actually measuring. External validity asks to what settings and populations findings can be generalised. Treatment prevalence asks how common the treatment or intervention is. Selection asks who receives the treatment and why.<\/p>\n\n\n\n

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This was important because it presented causal inference as more than a technical procedure. It is also a substantive sociological task. Understanding selection is part of understanding inequality. Who receives an intervention, who is excluded from it, and why those patterns emerge are not secondary details. They are part of the explanation.<\/p>\n\n\n\n

From diagnosis to policy relevance<\/h2>\n\n\n\n

The keynote also raised the question of how social stratification research can inform policy. Professor Triventi was careful here. He did not suggest that every study should end with simple policy advice. Instead, he argued that stratification research can contribute to policy by explaining mechanisms, defining concepts and indicators, identifying contextual enablers and blockers, estimating policy effects, detecting heterogeneous and unintended effects, and improving policy design.<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

This distinction matters. Stratification research is often very good at diagnosing inequality. It can show where inequality exists, how large it is, and which groups are affected. But the harder task is identifying feasible levers for reducing inequality. To do that, researchers need to connect diagnosis with mechanisms and intervention.<\/p>\n\n\n\n

One of the strongest messages here was that policy relevance does not mean making quick recommendations. It means producing knowledge that can explain mechanisms, identify conditions, assess effects, and improve the design of interventions.<\/p>\n\n\n\n

What kind of field should we build?<\/h2>\n\n\n\n

The keynote concluded with a clear agenda for the future of social stratification research.<\/p>\n\n\n\n

The field should keep theory and mechanisms central. It should move toward estimand-driven, transparent, and robust inference. It should triangulate across methods and data sources. It should build a cumulative scientific programme. It should reconnect diagnosis with intervention.<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

For me, the keynote\u2019s main message was that the future of social stratification research is not simply about bigger data or more advanced models. It is about building a more rigorous, theory-driven, and cumulative science of inequality.<\/p>\n\n\n\n

The best research will not necessarily be the research with the largest dataset or the most complex method. It will be the research that asks important questions, measures concepts carefully, explains mechanisms, tests claims transparently, and contributes to knowledge that matters.<\/p>\n\n\n\n

Personal reflection<\/h2>\n\n\n\n

As a participant at ECSR 2026, I found this keynote intellectually rich and very relevant to current debates in sociology and inequality research. It reminded me that methodological innovation should not move us away from the core sociological questions. Instead, new data and methods should help us ask those questions better. The keynote also encouraged me to reflect on my own research practice. Am I asking questions that connect to a wider cumulative agenda? Are my concepts clear? Are my analytical choices transparent? Do my findings contribute to broader knowledge about inequality?<\/p>\n\n\n\n

That, for me, was the value of Professor Triventi\u2019s keynote. It did not simply celebrate big data. It asked what kind of scientific field we want to build with it. The answer, as I understood it, is a field that is open to new data and methods but not driven by them alone. A field that values description but does not stop at description. A field that uses causal inference and prediction but keeps theory and mechanisms at the centre. A field that diagnoses inequality but also asks how inequality might be reduced. That is a challenging agenda, but also an important one.<\/p>\n\n\n\n

#ECSR2026 #SocialStratification #InequalityResearch #BigData #Sociology #SocialMobility #EducationInequality #ResearchMethods #CausalInference #DataTriangulation #AcademicConference #DublinConference #ComputationalSocialScience #EvidenceBasedPolicy #TheoryAndMethods #CumulativeKnowledge<\/p>\n\n\n\n

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