“It's Just a Matter of Time:” Lessons from Agency and Community Responses to Polar Bear-inflicted Human Injury
Bibliographic record
Abstract
Bear-inflicted human injuries or deaths are often widely publicised, controversial, and evoke substantial social responses that articulate public expectations about bear management. In this paper, we examine how local people and management agencies (i.e. Manitoba Conservation, Parks Canada, and the Town of Churchill) responded to a polar bear-inflicted human injury in Churchill, Manitoba, Canada. On November 1st, 2013, two people in Churchill were badly mauled by a polar bear. The incident shocked the community, highlighted problems such as a lack of bear safety education, and led to reviews of institutional policies for preventing polar bear-human conflicts. We used qualitative analysis methods to describe what is said (about polar bears, about people, and about management) and what is done (changes in behaviours and changes in policies/practices) when someone is attacked by a polar bear in Churchill. Results show that polar bear management agencies in Churchill respond remarkably well to errors in procedure, but are often unable to address the many underlying systematic drivers of polar bear-human conflict. Hence, managerial reactions to bear-human conflicts are successful at addressing the proximate cause of the problem, but offer few long-term solutions.
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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.034 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| 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".