The Relevance of Age and Gender for Public Attitudes to Brown Bears (Ursus arctos), Black Bears (Ursus americanus), and Cougars (Puma concolor) in Kamloops, British Columbia
Bibliographic record
Abstract
Abstract In British Columbia, brown bears (Ursus arctos), black bears (Ursus americanus), and cougars (Puma concolor) must relate to growing human populations. This study examines age- and gender-related attitudes to these animals in the urbanizing, agriculturally significant, intermontane city of Kamloops. Most respondents, especially women, feared cougars and bears, saw bears as more troublesome than cougars, and were concerned for child and adult safety. More middle-aged and older participants perceived brown bears as dangerous to companion animals, and black bears as troublesome, than did younger participants, and more middle-aged participants perceived brown bears as troublesome than did younger and older participants. Opinions favored trapping and removal of animals rather than shooting or toleration, but more younger participants opted for shooting, whereas more middle-aged and older participants opted for toleration and removal. Majorities agreed that the animals serve useful functions, women more than men for cougars, middle-aged more than old or young for bears, but saw only cougars as increasing their quality of life. These findings contribute to knowledge about human-wildlife relations, an important first step toward more efficient local and more general conservation 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 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) | 0.003 | 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".