Bibliographic record
Abstract
Hardly a day goes by without news of the extinction or endangerment of yet another animal species, followed by urgent but largely unheeded calls for action. An eloquent denunciation of the failures of Canada's government and society to protect wildlife from human exploitation, Max Foran's The Subjugation of Canadian Wildlife argues that a root cause of wildlife depletions and habitat loss is the culturally ingrained beliefs that underpin management practices and policies. Tracing the evolution of the highly contestable assumptions that define the human–wildlife relationship, Foran stresses the price wild animals pay for human self-interest. Using several examples of government oversight at the federal, provincial, and territorial levels, from the Species at Risk Act to the Biodiversity Strategy, Protected Areas Network, and provincial management plans, this volume shows that wildlife policies are as much – or more – about human needs, priorities, and profit as they are about preservation. Challenging established concepts including ecological integrity, adaptive management, sport hunting as conservation, and the flawed belief that wildlife is a renewable resource, the author compels us to recognize animals as sentient individuals and as integral components of complex ecological systems. A passionate critique of contemporary wildlife policy, The Subjugation of Canadian Wildlife calls for belief-change as the best hope for an ecologically healthy, wildlife-rich Canada.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.021 | 0.017 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".