Mapping social conflicts to enhance the integrated management of white‐tailed deer ( <i>Odocoileus virginianus</i> )
Bibliographic record
Abstract
Abstract Understanding the social feasibility of wildlife conservation approaches is essential to reducing social conflicts over wildlife and public backlash toward wildlife agencies and organizations. The Potential for Conflict Index 2 (PCI 2 ) and geospatial analyses of conflict can help wildlife practitioners strategically engage their publics, but these two tools have yet to be combined. Using data from a 2021 survey about white‐tailed deer in Indiana ( n = 1806), we analyzed conflict levels among stakeholder self‐identities and political ideologies regarding the acceptability of six possible management methods, three lethal and three nonlethal. We then conducted a hotspot analysis of gridded PCI 2 values to map areas of high and low social conflicts across the state. Conflict potentials showed more consistent covariation with political ideologies than with stakeholder self‐identities, aligning with urban–rural divides in wildlife experiences. Data on political leanings and residency may thus be more reliable than stakeholder categories to predict social conflicts over wildlife management. Hotspots of conflict over lethal methods clustered around urban areas, indicating that agencies should focus on engaging urban residents about deer management. Our conflict hotspots can be combined with other spatial data to create social units of analysis, which can help practitioners develop targeted and socially accepted strategies for 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".