Repair and Institutional Healing
Bibliographic record
Abstract
This symposium explores repair and institutional healing, marked by longer-term change efforts that blend emotions, multimodality, and broader societal impact. Institutional repair work aims to preserve valued aspects of institutions while modifying other aspects that are unwanted or unsustainable. Collectively, the presentations in this symposium explore how repair work represents one approach to reclaiming an institution’s integrity by rebuilding, renewing, or healing it, leading to positive societal outcomes. Future Cuts: Managing the Impact of Cultivated Meat on Traditional Food Institutions Author: Mia Raynard; U. of British Columbia Author: Vitaliano Barberio; USI (Lugano) Author: Magdalena Winkler; WU Vienna U. of Economics and Business Repairing Holes: How Communities Respond to Deinstitutionalization Author: Maggie Cascadden; U. of Alberta Author: Emily S. Block; U. of Alberta Theorizing Repair in Service to Analysis and Action: An Institutionalization Perspective Author: Jeannette Anastasia Colyvas; Northwestern U. Author: Hokyu Hwang; UNSW Sydney Repair and Healing as Varieties of Institutional Reform: Setting Up A Research Agenda Author: Madeline Toubiana; Telfer School of Management, U. of Ottawa Author: Brett Crawford; Grand Valley State 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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.056 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 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".