Research & Methodology
Interpretation, at every scale of data
IDeaS treats interpretation as running through analysis rather than following it — whether the work is an ethnography of a single team of analysts or a topic model over a corpus of millions of documents.
Our research agenda
Our work draws together three topics that are normally studied in separate scholarly communities. Holding them together is the point: the methods we use to study organizations are themselves organizational phenomena worth studying.
- New data-analytic techniques in the social sciencesThe reflexive and theoretically informed use of new data analytic techniques in the social sciences that leverage sophisticated algorithms such as topic modeling, natural language processing, and other forms of machine learning.
- The everyday work of data analysts in organizationsThe everyday work of data analysts in organizations — how they construct knowledge practices, and the epistemic infrastructures of organizations; both as an interesting ethnographic and qualitative topic in its own right, and as a means of encouraging our own reflexivity.
- The societal transformation attending the rise of dataThe societal, social, and cultural transformations attending the rise of data and analytics — including changing forms and interpretations of privacy and governmentality — to which social scientists should be able to speak.
Rendering
The community's founding vocabulary comes from Hannigan et al. (2019), which defines rendering as “a three-part process of generating provisional knowledge by iterating between selecting and trimming raw textual data, applying algorithms and fitting criteria to surface topics, and creating and building with theoretical artifacts.” Each stage involves choices that are interpretive, not mechanical.
- Rendering corpora What texts do you want to analyze — which authors, outlets, and dates? How do you preprocess them: stop words, stemming, n-grams?
- Rendering topics Which algorithm should you use, and how do you decide whether the model fits?
- Rendering theoretical artifacts How do you convert word-topic and topic-document matrices into something that can be interpreted and used to generate theory?
We actively embrace the active role interpretation inherently plays in data science and artificial intelligence. Put simply, we study interpretive data science empirically, and apply interpretive data science to generate new theory.Vern Glaser, opening the first IDeaS workshop, Edmonton, 2019
How the agenda has moved
The community's research agenda has moved in a discernible direction. The founding frame was rendering — the interpretive work involved in turning text into corpora, corpora into models, and models into theoretical claims. That question came out of computational text analysis and topic modeling.
From 2023 the emphasis shifted toward the ethnography of algorithms: what algorithmic systems actually do inside organizations, how routines form around them, and how judgment is redistributed when they arrive. By 2024 this had become a broader claim — that algorithmic organizing deserves theory of its own, rather than being treated as a setting in which existing theories can be applied.
In 2025 the lens widened again to assemblages, ecosystems, and fields, and with it a methodological problem: how to zoom in and out between a single practice and a whole field. In 2026 a political dimension became explicit — power, governance, accountability, and the question of who algorithmic systems serve.
The current live question, and a candidate theme for 2027, runs back toward the community's own practice: as human–AI research assemblages become increasingly bespoke, how do we make scholarly tooling social and institutional rather than forcing everyone into the same narrow set of tools?
Methods we work with
IDeaS is deliberately methodologically plural. What unites the work is the insistence that interpretation is not a stage that follows analysis — it runs through it.
- Topic modeling and computational text analysis. Used reflexively — with attention to how modeling choices shape the theory that comes out the other end.
- Organizational ethnography. Extended fieldwork on how data and algorithmic systems are built, used, resisted, and repaired in practice.
- Configurational and comparative methods. Approaches for moving between levels of analysis, connecting practices to ecosystems and fields.
- Narrative and semantic network analysis. Tracing meaning, framing, and the codification of professional norms across bodies of text.
- Archival and historical analysis. Mapping how fields, categories, and technologies emerge over time.
People
IDeaS has no formal membership. What follows are the people who have coordinated events and shaped the community's direction — and beyond them, the several hundred scholars who have presented, discussed, and taken part since 2019. Affiliations change; where you want a current biography, follow the link to a person's own institutional page.
Coordinating group
People who have organized or co-organized IDeaS events and set the community's direction.
Vern Glaser
University of Alberta
Co-founder; organized the 2019, 2023, and 2024 events.
Tim Hannigan
University of Ottawa
Co-founder and co-coordinator; led the 2025 Ottawa workshop.
P. Devereaux Jennings
University of Alberta
Co-founder and co-coordinator.
Mark Kennedy
Imperial College London
Hosted the 2026 London conference.
Christine Moser
Vrije Universiteit Amsterdam
Co-organizer, 2024; co-editor of the RSO volume.
Mayur Joshi
University of Ottawa
Co-organizer, 2025 and 2026.
Samer Faraj
McGill University
Co-organizer, 2026.
Shaila Miranda
University of Arkansas
Co-organizer, 2026.
Markus Perkmann
Imperial College London
Co-host, 2026.
Contributors and alumni
Scholars who have helped build the community, including those who organized early events as doctoral students at Alberta.
Deborah Anderson
University of Alberta
Co-organizer, 2023 and 2024.
Jennifer Sloan
Co-organizer of the 2019, 2023, and 2024 events.
Chris Steele
Co-organizer of the founding 2019 workshop.
Rodrigo Valadao
FGV EBAPE
Event planner for the founding workshop; long-time steward of the community's website and domain.
Yunjung Pak
University of Alberta
Registration coordination for 2026; presented at the London conference.