Biodiversity Governance Outside Protected Areas in the Context of Other Effective Area-Based Conservation Measures (OECMs): A Systematic Review
Bibliographic record
Abstract
The Other Effective Area-Based Conservation Measures (OECMs) have recently been recognized as a viable tool for conserving biodiversity beyond protected areas. As a new concept, it is essential to evaluate the current knowledge on OECMs and their prospects for conserving biodiversity resources outside protected areas. We conducted a systematic review of the literature on key concepts, including OECMs, governance, and biodiversity, using the Publish or Perish Software Program in Google Scholar. Out of the total 200 articles identified through the keywords, 54 were shortlisted for a comprehensive full-text review. Based on the closeness of the study objectives to our research questions, 27 articles were selected for detailed analysis. As no journal articles related to the OECM in Nepal were found within the set time frame, contemporary policies and legal documents of Nepal were also reviewed. Additionally, to account for the lengthy publication process, a few more recent journal articles were also reviewed. The review revealed that nearly half of the studies (13) focused on global and regional scales, while eight studies were conducted in eight different countries, and two studies in each of three additional countries. Recent studies on the integration of OECM principles in Nepal’s forestry sector policies and practices were also reviewed. Community-led conservation, supported by coordination and collaboration among multilevel governance systems - including both state and non-state actors-has been found effective in conserving biodiversity resources outside protected areas. However, further studies on natural resources governance beyond protected areas are needed to ensure long-term in-situ conservation of biodiversity through the OECM model.
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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 | 0.011 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.015 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".