Economics of conservation law enforcement by rangers across Asia
Bibliographic record
Abstract
Abstract Biodiversity targets, under the Kunming‐Montreal Global Biodiversity Framework, prioritize both conservation area and their effectiveness. The effective management of protected areas (PAs) depends greatly on law enforcement resources, which is often tasked to rangers. We addressed economic aspects of law enforcement by rangers working in terrestrial landscapes across Asia. Accordingly, we used ranger numbers and payment rates to derive continental‐scale estimates. Ranger density has decreased by 2.4‐fold since the 1990s, increasing the median from 10.9 to 26.4 km 2 of PAs per ranger. Rangers were generally paid more than the minimum wage (median ratio = 1.9) and the typical salaries in agriculture, forestry, and fishing sector (median ratio = 1.2). Annual spending on ranger salaries varied widely among countries, with a median of annual US71 km −2 of PA. Nearly 208,000 rangers patrolling Asian PAs provide an invaluable opportunity to develop ranger‐based monitoring plans for evaluating the conservation performance. As decision‐makers frequently seek an optimum number of law enforcement staff, our study provides a continental baseline median of 46.3 km 2 PA per ranger. Our findings also provide a baseline for countries to improve their ranger‐based law enforcement which is critical for their Kunming‐Montreal Global Biodiversity Framework targets.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".