Bibliographic record
Abstract
Although there has been a long-standing realization of the importance of interactions in institutional theory, typically, research has not focused on the unique contribution of this level of analysis. In our symposium we target interactions as a mechanism which contributes to our understanding of the process of institutionalization and institutional change. We focus specifically on the patterns of micro level interactions; the interplay between micro level interactions and macro level change and the way meaning is negotiated and constructed through interaction. We include four empirical as well as one theoretical paper and bring together a number of different streams in institutional theory. Following the presentations, Sahlin Andersson, an influential and major contributor to institutional theory will serve as a discussant. Interactions as Micro-Dynamics of Institutional Processes Presenter: Chad McPherson; U. of Iowa Configuration and Communitas in Institutional Change Presenter: Gazi Islam; Grenoble Ecole de Management Presenter: Charles-Clemens Rüling; Grenoble Ecole de Management Presenter: Elke Sybille Schüßler; Free U. Berlin Interactions as a Mechanism of Translation Presenter: Tamar Gross; Hebrew U. of Jerusalem Confronting Inequality and Overcoming Institutional Barriers Presenter: Johanna Mair; Hertie School of Governance Presenter: Miriam Wolf; Hertie School of Governance Presenter: Christian Seelos; Stanford U. Risk Talk and Institutional Change: The Case of Industrial Chemicals Presenter: Cynthia Hardy; The U. of Melbourne Presenter: Steve Maguire; McGill U.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.033 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".