Rediscovering the Power of Institutions: The MacroFoundations of Institutional Analysis
Bibliographic record
Abstract
Abstract This chapter provides a summary of the closing plenary at the 2018 Alberta Institutions Conference in which four scholars – Markus Höllerer, Marc Schneiberg, Patricia Thornton, and Charlene Zietsma – shared their views on how we could once again put the macrofoundations of institutional theory more center-stage in institutional analysis. The first major theme emerging from the panel discussion pertains to the meaning of macrofoundations. While Schneiberg sees institutions as socio-cognitive infrastructures, Zietsma emphasizes their constitutive nature. Second, both Thornton and Höllerer caution that an exclusive focus on either the micro- or the macro-level might remain only partial, and call for more cross-level studies of institutions – and for understanding the micro and the macro as co-constitutive analytical categories. Finally, the panelists discuss how we could break academic silos in institutional analysis and strive for theoretical innovation through interdisciplinary studies, among other avenues.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.045 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| 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".