Searching for “Superchief” and Other Fictional Indians: A Narrative and Case Comment on R v Bernard
Bibliographic record
Abstract
In R v Bernard, 2017 NBCA 48, the New Brunswick Court of Appeal upheld the lower courts’ reasoning that a Mìgmaw man living in the traditional Mìgmaq hunting territory of St. John, New Brunswick could not exercise his Aboriginal rights to hunt because he could not prove he descended from the particular subgroup of Mìgmaq who were at St. John at the time of contact with Europeans. In deciding so, the Court of Appeal rejected the argument that the Mìgmaq, as a nation, are the appropriate rights holders and ought to be the body deciding who can exercise the Mìgmaw right to hunt in the province. This argument was rejected based on the evidence of an expert historian who testified that Mìgmaq could not be a “nation” because they had a decentralized form of government and lacked a “Super Chief.” The case also exhibits undertones of floodgate fears of over-hunting as a consequence of finding the Mìgmaq nation to be the right-holders. This, however, ignores the role Mìgmaq laws and protocols will play in responsibly regulating Mìgmaq hunting and avoiding overuse of resources (not to mention the Crown’s ability to address conservation issues through the Sparrow justification framework). This article tells the story of the Bernard case and provides critical commentary on it.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.036 | 0.031 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.026 | 0.028 |
| Insufficient payload (model declined to judge) | 0.003 | 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".