Human–wildlife coexistence in a changing world
Bibliographic record
Abstract
Human-wildlife conflict (HWC) is a key topic in conservation and agricultural research. Decision makers need evidence-based information to design sustainable management plans and policy instruments. However, providing objective decision support can be challenging because realities and perceptions of human-wildlife interactions vary widely between and within rural, urban, and peri-urban areas. Land users who incur costs through wildlife argue that wildlife-related losses should be compensated and that prevention should be subsidized. Supporters of human-wildlife coexistence policies, such as urban-dwelling people, may not face threats to their livelihoods from wildlife. Such spatial heterogeneity in the cost and benefits of living with wildlife is germane in most contemporary societies. This Special Section features contributions on wildlife-induced damages that range from human perspectives (land use, psychology, governance, local attitudes and perceptions, costs and benefits, and HWC and coexistence theory) to ecological perspectives (animal behavior). Building on current literature and articles in this section, we developed a conceptual model to help frame HWC and coexistence dimensions. The framework can be used to determine damage prevention implementation levels and approaches to HWC resolution. Our synthesis revealed that inter- and transdisciplinary approaches and multilevel governance approaches can help stakeholders and institutions implement sustainable management strategies that promote human-wildlife coexistence.
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.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".