Common misconceptions of ‘other effective area-based conservation measures’ (OECMs) and implications for global conservation targets
Bibliographic record
Abstract
In 2022, nations committed to achieving a global target of protecting at least 30% of the Earth’s terrestrial and inland water areas and coastal and marine areas by 2030, as part of the Convention on Biological Diversity’s Kunming-Montreal Global Biodiversity Framework (Target 3 – the ‘30 × 30 protection target’ 1 ). This ambitious commitment has seen rapidly growing attention to ‘other effective area-based conservation measures’ (OECMs) as an additional means to protected areas to achieve the target. The OECM term was introduced into the Convention’s lexicon in 2010, but only formally defined in 2018 2 , with IUCN guidance published the following year 3 . There has been increased encouragement from the academic and conservation communities to use OECMs to contribute to global conservation targets 4 , 5 , 6 .
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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 | 0.014 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.053 | 0.061 |
| Insufficient payload (model declined to judge) | 0.010 | 0.009 |
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".