The Leadership Legacy of Commission Chairs: Building on and Extending a Comparative Study of Ten Canadian Commissions of Inquiry
Bibliographic record
Abstract
In 2014, Inwood and Johns analyzed the policy legacy of ten Canadian commissions of inquiry. This article extends that analysis by incorporating the policy legacy as one element of the leadership legacy of the commission chairs; the other two elements are the chair’s expressive legacy and fiduciary legacy. The expressive legacy can be that of a conservator, consolidator, entrepreneur, or catalyst, and the fiduciary legacy is determined by the commission chair’s respect for the norms and conventions of commissions of inquiry. Using the case studies from Inwood and Johns’ analysis of the commissions’ policy legacies, we ascribe expressive and fiduciary legacies to the chairs of the ten commissions. Through analysis of the relationships among the commissions’ policy legacies and the chairs’ expressive and fiduciary legacies, we explore the ways in which chairs’ conduct of the inquiry produce a leadership legacy for the chair.
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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 | 0.036 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.024 |
| Science and technology studies | 0.042 | 0.020 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.004 | 0.010 |
| 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".