David Calverley. Who Controls the Hunt? First Nations, Treaty Rights, and Wildlife Conservation in Ontario, 1783–1939.
Bibliographic record
Abstract
Over the past twenty years, many Canadian works have examined the impact of settler colonialism on Aboriginal rights to harvest natural resources. With Who Controls the Hunt? First Nations, Treaty Rights, and Wildlife Conservation in Ontario, 1783–1939, David Calverley contributes substantially to this literature by studying “changes in First Nations/state relations between 1800 and 1940, specifically as they pertain to Aboriginal hunting and trapping activity in Ontario.” (Fishing rights have been studied elsewhere.) There are four main interests: “the Anishinaabeg of northern Ontario, the federal or Dominion government (primarily the Department of Indian Affairs), the Ontario government (in the form of the Game and Fish Commission and its later manifestations . . . ), and the Hudson’s Bay Company” (4). Each had multiple perspectives that changed over time. The narrative reflects that complexity, discussing the motives, arguments, and actions of numerous people including bureaucrats, local magistrates, lawyers (private and government), fur traders and trappers, Aboriginal hunters, Hudson’s Bay Company directors, federal Indian agents, and sportsmen. Still, the author admits that “this was largely a battle between bureaucrats, with politicians appearing only at crucial moments” (164).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".