Investigating scientific, social and other influences on the 2017 British Columbia grizzly bear hunt ban
Bibliographic record
Abstract
British Columbia has the largest grizzly bear population in Canada. In 2017, B.C. banned the hunt of grizzly bears citing a lack of societal support, despite government-cited science that the hunt was sustainably managed. I explored the factors that influenced popular perceptions of grizzly bears, the hunt, how these factors may have influenced the province’s decision to implement the ban, and its reception by various actors. Methods included: examining key claims in government documents preceding the ban; surveying media coverage of the ban; and interviewing experts (n = 30) about their role in, and opinion of the ban. Results indicated that public perception of the hunt, and its framing as a trophy hunt outweighed scientific evidence of hunt sustainability. However, controversy over the representativeness of the “public opinion”, and comprehensiveness of government consultation processes remain. I suggest avenues for further research into roles of social values in natural resource policy.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".