Global status and emerging contribution of other effective area-based conservation measures (OECMs) towards the ‘30x30’ biodiversity Target 3
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
Other effective area-based conservation measures (OECMs) are sites outside of protected areas that deliver the effective, long-term conservation of biodiversity. Both protected areas and OECMs contribute to the implementation of the Global Biodiversity Framework’s Target 3, which calls for the conservation of 30% of marine, terrestrial and inland water areas by 2030. This paper provides the first global assessment of the contribution of OECMs to GBF Target 3. Between 2019 and 2023, 820 sites in nine countries and territories were reported to the World Database on OECMs, covering 1.9 million km 2 of the Earth’s surface and, in the terrestrial realm, contributing over 1% to the 30% coverage target. Notably, over 50% of reported OECMs are under governance by governments and less than 2% are governed by Indigenous peoples and local communities. In countries and territories that have reported OECMs, a far greater proportion of OECMs than protected areas are under shared governance (40.9% compared to 2.5%), and collaborative governance is the most common governance sub-type among reported OECMs. This paper finds that almost 30% of the 820 reported OECMs overlap with identified Key Biodiversity Areas, which are one global classification of areas of particular importance for biodiversity. With Target 3’s pressing deadline of 2030, there is an urgent need to scale up understanding and local to national engagement with the OECM framework, ensuring that it fulfills its potential to recognize diverse forms of equitable governance and effective conservation.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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