Bibliographic record
Abstract
This book probably would not have been written were it not for Morris Bates of the Vancouver Police Native Liaison Society. 1 He started talking to me in the days of my fleeting criminal and family legal aid practice out of Harry Fan's office in Chinatown.The court at 222 Main Street in Vancouver boasted of being the busiest in Canada but, with its clientele of Natives, immigrants, and obviously compromised people, much of what happened there did not make sense to me.When I left for Montreal to do a master's degree in international law, Wanda John pleaded "do something for us."I was bewildered.Just what could I possibly contribute?When I stopped to visit someone on the Blood Reserve at Standoff, the Horn Society was conducting ceremonies.They painted my face and encouraged me to try.The children were all so bright and beautiful.How could I deny such shining promise?When I reached the Université du Québec à Montréal, the late Katia Boustany drew on her Palestinian heritage, directing me immediately to the issue of exclusion.I was mystified at first.Peter Leuprecht insisted quietly on the new international legality.William Schabas suggested a master's topic that proved much more revealing than I expected, and Georges LeBel's supervision provided the acute insights and encouragement needed to complete "Canada v.The Haudenosaunee (Iroquois) Confederacy at the League of Nations: Two Quests for Independence.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.421 | 0.288 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".