<i>The Coxford Lecture</i> Seek the Good: Professional Trust, Justice, and the Rule of Law
Bibliographic record
Abstract
When I was asked to serve as Governor General of Canada, I discovered I had much to relearn. Despite my background as a law student, professor, and dean, I had to revisit the legal principles and conventions of our constitution, especially as they relate to the authority and function of the Crown in Canada. My re-education enabled me to develop an even deeper admiration for how precious the rule of law is in our country, how thin and vulnerable its veneer can be, and how vital it is in moving closer to justice. Even more, my re-education revealed for me anew that the study and practice of law is a hollow pursuit if we do not use it to strive toward justice.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.016 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".