New Directions in the Study of Institutional Logics: From Tools to Phenomena
Bibliographic record
Abstract
In this article, we take stock of the institutional logics perspective and highlight opportunities for new scholarship. While we celebrate the growth and generativity of the literature on institutional logics, we also note that there has been a troubling tendency in recent work to use logics as analytical tools, feeding disquiet about reification and reductionism. Seeding a broader scholarly agenda that addresses such weaknesses in the literature, we highlight nascent efforts that aim to more systematically understand institutional logics as complex, dynamic phenomena in their own right. In doing so, we argue for more research that probes how logics cohere and endure by unpacking the role of values, the centrality of practice, and the governance dynamics of institutional logics and their orders. Furthermore, we encourage bridging the study of institutional logics with various literatures, including ethnomethodology, phenomenology, professions, elites, world society, and the old institutionalism, to enhance progress in these directions.
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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 | 0.034 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.005 | 0.115 |
| Scholarly communication | 0.028 | 0.081 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.008 | 0.018 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".