The human dimensions of Newfoundland and Labrador's murre hunt: a social-ecological study
Bibliographic record
Abstract
A human dimensions approach to wildlife management was used to solicit murre hunter and stakeholder perspectives, attitudes, and recommendations for current Common Murre (Uria aalge) and Thick-billed Murre (Uria lomvia) harvest management practices in the Canadian province of Newfoundland and Labrador (NL). NL is the only jurisdiction that administers a legal, non-Indigenous murre hunt in North America. The overarching goals of this study were formulated to help inform potential changes for upcoming murre hunting seasons. A qualitative approach was utilized by conducting one-on-one interviews designed to collect hunter and stakeholder input on current murre harvest regulations and practices, provincial murre population status, alternative management strategies, the social and cultural significance of the murre hunt, the perceived extent and impact of illegal harvesting, and hunters’ participation in the CWS National Harvest Survey. Results indicated that over-harvesting, lack of enforcement, illegal activity, limited access to population data, species harassment, and lack of community engagement were frequently reported hunter concerns. Interview analyses conclude that hunter input provides valuable local knowledge for resource managers to inform future harvest seasons. However, it also indicated a strong desire for more mechanisms for harvester feedback and input to secure the delivery of apt environmental policies.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".