Bibliographic record
Abstract
This study used interviews and surveys to determine how provincial Department of Natural Resources (NSDNR) staff, Aboriginal participants, and members of stakeholder groups (hunters/trappers, agriculturalists, non-consumptive) perceive the management of black bears in the province of Nova Scotia. NSDNR staff members were generally satisfied with the department’s management of black bears but expressed a desire for increased education, research, and population monitoring. Within and between the stakeholder groups, opinions varied about NSDNR’s practices and controversial bearmanagement issues. Opinions on certain controversial practices (hunting over bait, hunting with hounds, spring hunting, and sale/export of bear gall-bladders) were gathered and most groups only supported hunting over bait and the sale/export of bear gallbladders. For dealing with bear-human conflicts, staff members’ approach to handling situations generally coincided with the approach desired by stakeholders. The results show a need for increased public outreach by NSDNR to determine why stakeholders’ opinions are divided. The results are useful to wildlife managers elsewhere because they highlight areas of agreement and disagreement among stakeholder groups, and provide insight into how departmental staff perceive management practices.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".