Connections and Opportunities for Integration Between Carnegie Perspective and Institutional Logics
Bibliographic record
Abstract
Although the Carnegie perspective on organizations helped lay the theoretical foundation of the growing and influential literature on institutional logics, the dialogue between researchers contributing to these two important lines of organizational research remains limited. This is unfortunate because these bodies of work are among the most vibrant in contemporary organizational research and stand to benefit from closer integration. For example, research on logics could take a more micro-to-macro approach to the study of institutional pluralism by drawing on a view of the organization that gives greater centrality to mechanisms underlying the decision-making process. On the other hand, Carnegie research that seeks to explain outcomes such change and search and views goals as a key concept guiding interpretation and action could draw on research on logics to advance understanding of the external processes that influence the selection of goals. Although small steps have been taken in these directions, this symposium aims to create additional opportunities for integration by strengthening dialogue among key contributors to these two influential lines of work.
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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 | 0.039 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.011 | 0.084 |
| Scholarly communication | 0.026 | 0.046 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.009 | 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".