Levels of conflict over wildlife: Understanding and addressing the right problem
Bibliographic record
Abstract
Abstract Human–wildlife conflicts are complex and defy simple explanations and solutions. The fields of conflict analysis and peacebuilding offer insights into the intensity, intractability, and possible approaches to addressing different kinds of conflict. Building on these fields, as well as advances in conservation practice, we adapt a framework for human–wildlife conflict that consists of three levels of conflict over wildlife: Level 1 conflicts are disputes over issues such as crop or livestock loss or concerns about safety, yet typically involve relatively high tolerance of the damage‐inducing species. In level 2 conflicts, in addition to visible impact of wildlife, there is a history of unsatisfactory attempts to address these issues, creating underlying resentment, tensions, and a sense of injustice among at least one of the parties. Level 3 conflicts are deep‐rooted and become intertwined with the identities of the parties and community involved, and extend to broader tensions over social identities and clashing values and beliefs. Such conflicts require mediated reconciliation dialogues and conflict transformation approaches. A structured understanding how to address a conflict before it escalates to a deeper level is fundamental for managing conservation challenges as complex and dynamic as conflicts over wildlife.
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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 | 0.014 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.009 | 0.044 |
| Scholarly communication | 0.017 | 0.032 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".