The Adjudication of Historical Evidence: A Comment and and Elaboration on a Proposal by Justice Lebel
Bibliographic record
Abstract
The appropriate forum and procedures for deciding whether Aboriginal and treaty rights exists has been troubling for courts. In the early years after the enactment of section 35, and today, in a significant number of cases, the issue was decided in criminal proceedings. Often charges were laid for hunting or fishing without a license, or out of season. Such was the case in R. v. Marshall and R. v. Bernard when the Supreme Court of Canada was required to consider an appeal of a conviction for a provincial offence related to logging. Justice LeBel mused about the appropriateness of criminal proceedings to determine matters that had wide spread consequences on people who were not parties to the proceedings. This article looks at the alternative of using civil proceedings to address these matters, and tentatively concludes that it would be feasible if supports, such as adequate funding, were put in place. However, the article raises a larger concern with the approach the Supreme Court of Canada takes on history. In this case, and in others, the Court is attempting to read history in order to make a determination on the contemporary balance between Aboriginal and non-Aboriginal access to resources. This approach necessarily distorts history and sometimes results in puzzling conclusions. The article ends by contemplating a future process that could give full recognition to historical realities in order to inform contemporary rights.
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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 | 0.052 | 0.103 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.030 | 0.039 |
| Scholarly communication | 0.020 | 0.015 |
| Open science | 0.012 | 0.009 |
| Research integrity | 0.100 | 0.093 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".