Evolving Our Understanding and Practice in Addressing Social Conflict and Stakeholder Engagement Around Conservation Translocations
Bibliographic record
Abstract
The conservation field has evolved to include an understanding of human values and attitudes toward wildlife; however, there is still too little emphasis on, and prioritization of, building understanding of the complex and context-specific social conflicts among people and groups involved with or impacted by conservation actions, including translocation. Both foci add value, but the latter is critical for building receptivity for conservation efforts and more thoughtfully designing appropriate context-specific processes for stakeholder engagement and shared decision-making. A deeper analysis of the social conflict dynamics involving the human relationships among individuals and groups engaged in a conservation conflict is needed as a first step in paving the way for the long-term success of conservation projects. Using a “Levels of Conflict” model offers a starting place for the analysis of social conflict often underpinning conservation translocation efforts. Further, we recommend employing a Conservation Conflict Transformation approach when considering conservation translocations to ensure that stakeholder engagement processes sufficiently engage the system, reconcile deep-rooted conflict among those involved and offer the best chance for shared progress and conservation success.
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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 | 0.143 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.022 | 0.105 |
| Scholarly communication | 0.034 | 0.057 |
| Open science | 0.011 | 0.034 |
| Research integrity | 0.025 | 0.037 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".