Community-led marine OECMs: Assessing enabling regulatory frameworks and potential cases in Indonesia
Bibliographic record
Abstract
The Convention on Biological Diversity’s Kunming-Montreal Global Biodiversity Framework (KM-GBF) calls for conserving at least 30 percent of the planet through protected areas or Other Effective Area-based Conservation Measures (OECMs) by 2030. OECMs can complement Marine Protected Areas by recognising diverse forms of management delivering biodiversity benefits regardless of their objectives. A key barrier to their implementation is a lack of legal clarity on OECM identification, recognition, and monitoring at the national level. To address this, we examine Indonesia’s marine and forestry regulations in the context of OECM criteria, identifying opportunities to adapt existing policies to support the recognition of community-led marine areas as OECMs. These regulations generally align well with Criterion A (non-protected area) and Criterion B (active governance), but gaps remain in addressing effectiveness in conserving biodiversity (Criterion C) and associated ecosystem services and socio-cultural values (Criterion D). Building on this analysis, we evaluated three Locally Managed Marine Areas in Indonesia to assess how the OECM framework could support on-ground management practices. These case studies showed conservation effectiveness, with increases in resource availability (e.g. >65% more catch in two sites). Our findings underscore OECMs’ potential as inclusive, adaptable models for advancing biodiversity targets in Indonesia and beyond.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".