Closing staffing gaps in Madagascar's protected areas to achieve the 30 by 30 conservation target
Bibliographic record
Abstract
Abstract Protected areas (PAs) guard critical biodiversity and provide ecosystem services, serving as a pillar of the Kunming‐Montreal Global Biodiversity Framework that aims to protect 30% of the planet by 2030. But most PAs are understaffed. This study documents external workforce contributions to PA staffing in Madagascar, a biodiversity‐rich country that tripled its PA network in 2015. Taking a novel multi‐level approach, we use online surveys of 44 PAs and 13 institutions (managing 81% of PA surface area in Madagascar). Results reveal severe understaffing, reaching only a third of the global recommendation at just one staff member per 37.3 km 2 . Longer‐established PAs enjoy higher staffing ratios. Local community members comprise 94% of the PA external workforce, contributing up to 52% of labor in category V and VI PAs. Evolving human resource policies to deliberately better engage local communities will build PA resilience, addressing staffing gaps in a cost‐effective and sustainable manner to achieve the 30 by 30 target.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".