Community-led marine OECMs: Assessing enabling regulatory frameworks and potential cases in Indonesia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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 it