Human–elephant conflict mitigation as a public good: what determines fence maintenance?
Bibliographic record
Abstract
Negative interactions between humans and elephants are known to have serious consequences, resulting in loss of life and deterioration in the quality of life for both species. Reducing human–elephant conflicts (HEC) is essential for elephant conservation as well as social justice. Non-lethal electric fences placed around villages or communities are a widely used intervention to mitigate HEC. Such barriers act as non-excludable and non-subtractable resources—i.e., public goods—that must be maintained collectively by beneficiaries or the State. Despite being fairly effective when well maintained, most such fences in northeast India are poorly maintained. This leads to our central question: why are some fences well maintained and others poorly maintained? We studied 19 such fences using qualitative comparative analysis, Ostrom's social-ecological systems framework, and a grounded theory approach, incorporating qualitative social science tools. We found that, contrary to our hypothesis, the functionality of fences cannot be predicted based on the design of the fence, whether or not the community made cash payments, or ethnic homogeneity or leadership in the village. Instead, we found there are three potential pathways of maintenance: (1) a community maintainer, (2) the community self-organizes, and (3) the Forest Department. Maintenance occurs when there is a congruence between perceived costs and benefits for the entity responsible for fence maintenance. These costs and benefits are diverse, including not just material benefits but intangibles like goodwill, sense of safety, social standing, and a feeling of fairness. We highlight these factors and provide recommendations for practitioners and policy.
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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".