Bibliographic record
Abstract
My exposure to the Canadian legal system over the last twenty years has led to this book about an issue of considerable public interest and of great importance to Aboriginal peoples: the ways in which Aboriginal oral narratives, commonly called oral histories or oral traditions, have been used in Canadian courts.The Indigenous Bar Association and a panel of elders from across Canada have met on several occasions, sometimes with federal justices and other officials and interested parties, including myself, to query the conditions under which oral narratives can be entered as evidence, and Aboriginal elders can appear in court.Many other people, Aboriginal and otherwise, have expressed their own deep concerns both in print and in private.The overriding worry is that without oral narrative evidence, Aboriginal people cannot adequately present their own evidence in litigation.My interest is in the larger issues rather than the views of particular people, although I consider many individual views in detail within these pages.I have used, at length in some cases, opposing approaches and points of view not to be critical of their authors, but rather to explore the conflicts and identify possible resolutions that employ the best scholarship and social responsibility to all the parties involved.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.355 | 0.132 |
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".