Assessing the Prospects for Community-Based Wildlife Management: The Himalayan Musk Deer (<i>Moschos chrysogaster</i>) in Nepal
Bibliographic record
Abstract
Community-based wildlife management has been the subject of debate in recent years. We argue for more ex ante assessments of community-based wildlife management to distinguish good prospects from poor ones. We develop a conceptual framework, the Collective Resource Management framework, to carry out such evaluations and apply it to a case study community in northeastern Nepal that involves live capture and extraction of musk from the endangered Himalayan musk deer (Moschos chrysogaster). We find that the case study presents a number of favorable conditions conducive to community-based wildlife management but with several serious limitations. For example, the Nepali government must be willing to allow this activity on a legal basis, because wildlife exploitation has not been allowed within national parks historically; however, there are signs this may be changing. More importantly, the presence of a strong tourism industry in the case study community makes alternative economic activities like musk extraction less attractive, in comparison to poorer regions of Nepal where there is no tourism. The Collective Resource Management evaluation approach shows promise as a structured method to check the viability of community-based wildlife management projects before they are initiated.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".