Levels of conflict over wildlife: Understanding and addressing the right problem
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 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.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| 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