Unpacking OECMs: Pathways for Effective Implementation and Operationalization in Asia
Bibliographic record
Abstract
This paper examines the operationalization of Other Effective Area-Based Conservation Measures (OECMs) in Asia, highlighting their transformative potential for achieving inclusive biodiversity conservation, climate resilience, and sustainable development goals.It also emphasizes the potential of OECMs to advance the reporting of Target 3 of the Kunming-Montreal Global Biodiversity Framework (KM-GBF), showcasing their role in advancing integrated, area-based conservation approaches across diverse ecosystems.Drawing on over a decade of collaborative efforts facilitated by the Asia Protected Areas Partnership (APAP), especially focusing on OECMs since 2023, the study synthesizes insights from national dialogues in Bangladesh, Thailand, Vietnam, Sri Lanka and the Republic of Korea, along with an APAP regional workshop on OECMs in Japan.It identifies key challenges in governance, spatial integration, and monitoring and proposes actionable solutions tailored to the region's socio-political and ecological contexts.Emphasizing the integration of participatory governance models, advanced monitoring frameworks, and cultural and socio-economic values, the paper situates areas reporting as OECMs as pivotal towards enhancing ecological connectivity and fostering inclusive conservation strategies.By aligning global guidance with regional, national, and local realities, this study provides a roadmap for effectively operationalizing OECMs in Asia 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 | 0.077 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.002 | 0.004 |
| 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".