FIRST NATIONS’ MOOSE HUNT IN ONTARIO: A COMMUNITY’S PERSPECTIVES AND REFLECTIONS
Bibliographic record
Abstract
Moose ( Alces alces ) hunting and other means of forest food production employed by members of First Nations communities are undertaken as part of their treaty rights in Ontario, articulated in specific nation-to-nation agreements with the Government of Canada on behalf of the British Crown. Aroland First Nation in Northwestern Ontario is party to Treaty 9 (1905), which overtly protects the community ’ s rights to hunt throughout the unoccupied tracts of Crown land claimed as “traditional territory.” Traditional use supersedes provincial authority and, as such, is not managed by provincial policy or regulation. This jurisdictional divide has presented an interesting history and many challenges for both provincial managers and First Nations land users. Strained relationships between provincial authorities and First Nations, emergent from decades of misunderstandings of jurisdictional authority, have presented difficulty in all aspects of natural resource management. In this paper, we engaged community-based researchers in an exploration of the community ’ s perspective of the current and historical management regime. We also quantified the annual moose harvest by the First Nation, an assessment that is never undertaken by provincial managers; our results show a 40% error in provincial calculations. This error could have significant implications for future moose populations, as well as wildlife managers and both provincial and First Nations hunters. In collaboration with community members, we interpret the results, discuss implications, and provide recommendations for future consideration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.032 | 0.015 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| 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".