Bibliographic record
Abstract
Telling It to the Judge is Professor Arthur Ray's account of his experiences of telling "it" -that is, the history of Aboriginal peoples -to the judge in court.Dr Ray's engaging story makes clear that he has brought his considerable skills to the courtroom at the request of several lawyers, including me.Historical work in court is a task unsought and largely unrewarded, and not of the historian's own design.After all, it is not historians who initiate court cases to resolve Aboriginal lands and resources claims.They generally research, write, publish, and teach.I can think of no historian who has taken up history and academia as a profession with the goal of having his work judged in court.However, litigating history is precisely what Canadian courts are now engaged in.More than forty aboriginal rights cases have gone to the Supreme Court of Canada since 1982, and each has engaged at least one historian -usually more.This is because the courts work on an adversarial system, the basis of which is the testing of evidence through intense and extensive cross-examination.From Aboriginal people judges hear stories previously unknown, and the stories are always longer than the history of Canada as a nation-state.Canada and the provinces are the newcomers on the block.The other side in aboriginal rights cases is the Crown, represented by the federal or provincial government, or both.Lawyers for the Crown are assigned the task of proving that the claimant aboriginal group in court has always had less, known less, claimed less, used less, and perhaps never existed at all.There are real consequences to these contests.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.725 | 0.678 |
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".