Defining ‘long-term’ for protected areas and other effective area-based conservation measures (OECMs)
Bibliographic record
Abstract
The concept of ‘long-term’ is a key part of the definitions of both protected areas and other effective area-based conservation measures (OECMs). Here we outline existing global guidance on the interpretation of ‘long-term’ for area-based conservation and clarify aspects that may have led to misinterpretation. As national governments rapidly seek to define OECMs in response to the raised ambitions of the Convention on Biological Diversity’s Kunming-Montreal Global Biodiversity Framework (GBF), there will be increasing interest in what counts towards Target 3 of the GBF. Ultimately, more land managed for conservation is good and all forms of area-based conservation should be encouraged. However, not all forms of area-based conservation qualify for inclusion in Target 3. Long-term intent and outcomes are fundamental, as outlined in the definitions of protected areas and OECMs.
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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 | 0.019 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".