Community Perspectives on Elephant Conservation in Eastern Nepal
Bibliographic record
Abstract
Understanding people’s attitudes towards elephants (Elephas maximus) is crucial for formulating appropriate policies for species conservation and mitigating human-elephant conflict (HEC). Therefore, this study aimed to assess attitudes and perceptions toward elephant conservation in Udayapur District, eastern Nepal. Based on information from key informants (n = 10) and focus group discussions (n = 3), a total of 97 households were selected for a semi-structured questionnaire survey to collect data on human-elephant incidents. Half of the respondents (50%) identified crop damage as the primary issue caused by wild elephants, and nearly half (46%) reported an increase in HEC over the past five years (2016–2020). The majority (60%) claimed habitat encroachment as a major cause of HEC in the study area. Approximately 46% of respondents use fire-related techniques to mitigate such conflicts. Moreover, more than half of the respondents (62%) showed a low willingness to conserve elephants, which was significantly influenced by their education level [χ2 (2) = 9.43, p < 0.001] and occupation [χ2 (2) = 7.81, p < 0.05]. The findings of this study will help develop management interventions that benefit communities and elephants through effective HEC mitigation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".