Understanding conflict and consensus regarding wood bison management in Alaska, USA
Bibliographic record
Abstract
Context Wood bison (Bison bison athabascae) have been absent from Alaska for over 170 years. In the spring and summer of 2015, however, 130 animals were reintroduced to the state. These wood bison were restored through a consensus-based planning process, but it remains unknown how the animals will be managed. Aims To survey urban and rural Alaska residents to understand the effect of proximity to the resource on residents’ preferences for management of wood bison in different scenarios. Methods Data were collected in urban areas using a mail-back questionnaire (n = 515) and by on-site interviews with rural residents (n = 31), between June and September 2015. Respondents were asked to state their preferred wood bison management strategies under specific situations of potential human–bison conflict. Key results Residents from urban and rural study areas differed in their preference of bison management, particularly in more severe situations (i.e. damage to property, causing injury to people). Conclusions Urban and rural residents were reluctant to use lethal management of wood bison, even under situations that threaten human property. Implications Backlash from urban residents could occur if managers use lethal management. Rural residents, however, favour lethal management when human injury occurs.
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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 | 0.003 | 0.006 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".