Commentary on Perspective Article: ‘Institutional Logics: Motivating Action and Overcoming Resistance to Change’ – Heather A. Haveman, David Joseph-Goteiner, and Danyang Li
Bibliographic record
Abstract
Abstract Haveman, Joseph-Goteiner, and Li's (2023) perspective article contributes important insights into China's transition away from central planning and redistribution toward greater market coordination of economic exchange. In our commentary on their insightful article, we build on and extend their arguments in three main ways. First, we discuss how future studies might extend the authors’ work by leveraging the ‘messiness’ of institutional change to explore the cross-level dynamics involved in transforming institutional logics. Second, we build on the authors’ call for more historically grounded, contextualized research on institutional logics to argue that the conditions surrounding logic emergence have important implications for inter-logic dynamics and organizational responses. Third, we build on the authors’ suggestions for future research to underscore the broader consequences of institutional logics and their potential to perpetuate or exacerbate social inequalities and other societal challenges.
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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 | 0.009 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.040 | 0.042 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".