Mapping social conflicts to enhance the integrated management of white‐tailed deer ( <i>Odocoileus virginianus</i> )
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it