Pt 4B Advising First Nations and Govt - His Brilliant Legacy: A Conference in Honour of Peter Hogg
Bibliographic record
Abstract
His Brilliant Legacy: A Conference in Honour of Peter W. Hogg was held on January 10, 2024 at Osgoode Professional Development and co-hosted by Osgoode Hall Law School and Blake, Cassels & Graydon LLP.\nThis session includes: "Advising First Nations and Government"\nThis panel considers Peter Hogg's contributions to public life, from his influence on the New Zealand Bill of Rights, to his role as an advisor to First Nations, to the Governor General of Canada, to the federal judicial appointments process, and to the process of constitutional reform.\nChair: Jamie Cameron, Professor Emerita, Osgoode Hall Law School\nSpeakers: Professor Erin Crandall, Political Science Department, Acadia University, Mr. Dave Joe, Legal Advisor, Yukon, BC, and NWT First Nations, Professor Emmett Macfarlane, Political Science, University of Waterloo, Professor Emeritus Kenneth Keith, Victoria University of Wellington Te Herenga Waka
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.074 | 0.018 |
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".