Why do EDI policies fail? An inhabited institutions perspective
Bibliographic record
Abstract
Purpose Equity, diversity and inclusion (EDI) policies have proliferated in recent decades, but studies have repeatedly noted their inefficacy and adverse effects. To understand the potential root causes of the inefficiency of EDI policies, this study examines how they are inhabited by individuals at the ground level. Design/methodology/approach This study draws on data gathered through 23 in-depth interviews with instructors at Progressive U, a large research-intensive Canadian university. Findings The data gathered/analyzed suggest that the implementation of EDI policies at Progressive U is hindered by the absence of coercive enforcement mechanisms, skepticism about their authenticity, the over-regulation of work and unresponsive bureaucratic structures. Originality/value This study examines the implementation of EDI policies through the prism of the inhabited institutions perspective in organizational sociology, producing insights that help to explain why EDI policies typically fail. In doing so, it produces insights relevant to both academic researchers and practitioners in the field.
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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 | 0.020 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.046 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".