To Dignity Through the Back Door: Tsilhqot’in and the Aboriginal Title Test
Bibliographic record
Abstract
The decision of the Supreme Court in Tsilhqot’in clarified the content of the test for Aboriginal title. This article examines the reasoning of the Court to determine whether the judgment meaningfully contributes to re-equilibrating the conceptual framework of Canadian Aboriginal jurisprudence, still deeply skewed in favour of the Crown and of its asserted sovereignty. Beyond the case’s positive outcome for the Tsilhqot’in people, does the clarification of the test empower Indigenous peoples seeking the recognition of their title over their traditional territory? Regarding the role reserved by the Court to Indigenous legal traditions in its reasoning, the answer is no: in determining the standard to meet to establish title, the Court continues to treat “Aboriginal perspectives” as a source of evidence rather than law. Indeed, it draws solely on common law private property precedents to determine what counts as “occupation” sufficient to ground title. However, if we examine the content of this common law standard as clarified by the Supreme Court, the outlook becomes much more positive. By decisively associating the main legal issue underlying title with the historical control of the land rather than with the manner or intensity of its use, Tsilhqot’in effectively makes room for a robust recognition and respect of the continued agency of Indigenous societies on their traditional territories.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.016 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".