International Progress in Protected Area Policy and Practice during 2024
Bibliographic record
Abstract
2024 has been an important year for global protected area policy, with major Conferences of Parties for four relevant global conventions and the first opportunity for stocktaking since agreement of the Convention on Biological Diversity’s (CBD) Kunming- Montreal Global Biodiversity Framework (GBF) in late 2022. Global coverage of both protected areas and OECMs has increased in this period, but not fast enough to be on track for meeting the targets set for 2030. Outside government, there were important gatherings of people involved with human rights and conservation, protected area rangers and wilderness advocates, all with implications for the development of national protected area networks, and for implementing the GBF’s Target 3 (30x30) for protecting 30% of land and ocean by 2023. Finally, some significant steps towards implementation of 30x30 were completed or underway.
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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 | 0.052 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".