Field or Fields? Building the Scaffolding for Cumulation of Research on Institutional Fields
Bibliographic record
Abstract
The concept of an institutional field is one of the cornerstones of institutional theory, and yet the concept has been stretched both theoretically and empirically, making consolidation of findings across multiple studies more difficult. In this article, we review the literature and analyze empirical studies of institutional fields to build scaffolding for the cumulation of research on institutional fields. Our review revealed two types of fields: exchange and issue fields, with three subtypes of each. We describe their characteristics, and subsequently, review field conditions in the extant literature and develop a typology based on two dimensions: the extent of elaboration of institutional infrastructure and the extent to which there is an agreed-upon prioritization of logics. We discuss the implications of field types and conditions for isomorphism, agency, and field change, based on a review of the literature that revealed six pathways of field change and the factors affecting them. We outline a research agenda based on our review highlighting the need for consolidation of field studies and identify several outstanding issues that are in need of further research.
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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 | 0.089 | 0.186 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.021 | 0.014 |
| Science and technology studies | 0.005 | 0.048 |
| Scholarly communication | 0.019 | 0.057 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".