Predicting positive outcomes for waterfowl hunters and waterfront residents
Bibliographic record
Abstract
ABSTRACT Social conflicts among wildlife stakeholders can suggest possible new directions for wildlife management, including opportunities to expand the base of stakeholders supporting active management. In response to New York State Department of Environmental Conservation information needs, we examined potential conflicts between waterfowl hunters and waterfront residents to understand their attitudes toward hunting along developed waterfronts and how spatial proximity was related to likelihood of waterfowl hunters’ experiences of harassment by waterfront residents. We sent mail‐back questionnaires to waterfowl hunters (n = 1,000) and waterfront residents (n = 1,000) near Lake Ontario in the greater‐Rochester area of New York, USA. We identified factors predicting acceptance of waterfowl hunting along developed waterfronts. Waterfront residents who knew waterfowl hunters were more supportive of waterfowl hunting than residents who did not know hunters. Hunters who hunted closer to occupied dwellings (e.g., waterfront homes) were more likely to experience harassment from residents than hunters who hunted farther away. Educational communication and policies that address public access, safety, safe distance of hunting from homes, and rules and regulations relating to waterfowl hunting are needed for acceptance of waterfowl hunting along developed waterfronts. Non‐hunters who accept hunting activities have the potential to positively affect wildlife management by expanding the base of involved, supportive stakeholders. © 2018 The Wildlife Society.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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".