Quantifying the Influence of Emotions on Management Acceptability for White-Tailed Deer<i>(Odocoileus virginianus)</i>
Bibliographic record
Abstract
Emotions pervade human-wildlife relationships across social identities and cultures. Yet research on how emotions influence the cognitive processing of wildlife encounters remains sparse. In this study, we quantify the role of anticipated emotions in processing hypothetical encounters with white-tailed deer (Odocoileus virginianus). In 2021, we surveyed Indiana residents about deer and deer management (n = 1.806). Under four hypothetical deer encounters, we estimated the structural relationships among respondents’ general attitudes toward deer, mutualism wildlife beliefs, scenario-specific emotions, and scenario-specific lethal control acceptability. Emotions mediated 14% of the effect of general attitudes on lethal control acceptability when encountering a fawn and completely mediated this effect when encountering a diseased deer. Our findings suggest that emotions work together with cognitions to process stimuli in a human-wildlife encounter and make a normative decision. Accounting for emotions in decision-making will help practitioners develop more effective and socially accepted approaches to wildlife conservation and management.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".