Taking stock of Institutional Theory using Topic Modeling
Bibliographic record
Abstract
This paper uses topic modeling and network methods to review the recent institutional theory literature and reconcile the growing diversity of research streams. Through an analysis of vocabularies used in the abstracts of published articles since 2002, we identify 15 unique topics in the literature that correspond with different modes of theorization in institutional theory. Using network methods, we compare words and topics in terms of their centrality in recent research. Based on our findings, we advocate for (1) the development of integrative theory on agency in institutional processes, for (2) the use of mixed methods research, for (3) the use of the concepts of culture and rhetoric to bridge the paradigmatic ravines between structure vs. agency perspectives and change vs. stability perspectives, and for (4) the development of theory on under-researched phenomena, such as failed institutionalization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.027 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.020 | 0.017 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".