Coverage and beyond: how can private governance support key elements of the Global Biodiversity Framework’s Target 3?
Bibliographic record
Abstract
A vast cross-societal effort will be needed to achieve the ambition of protecting and conserving 30% of the earth’s lands and oceans by 2030, as called for in Target 3 of the Kunming-Montreal Global Biodiversity Framework. While focus is often given to the 30% coverage aspect of this target, other elements – on the location and effectiveness of protected and conserved areas – are equally important. As the implementation of Target 3 progresses, it is increasingly acknowledged that non-profit organisations, for-profit organisations, and individual landowners play a key role by choosing to manage their lands and waters to deliver conservation outcomes. However, privately protected and conserved areas lack recognition by many governments charged with reporting progress on the target. For countries and territories where these areas have been reported, we use the World Database on Protected Areas to explore their contribution towards elements of Target 3, particularly coverage, connectivity and ecological representation. In addition, we explore how privately governed ‘other effective area-based conservation measures’ contribute to Target 3 in countries and territories where they have been identified. Our results demonstrate that privately protected and conserved areas play a significant role in some countries’ efforts to meet Target 3. Since these areas are known to be under-reported, we stress the need for scaled up efforts for their recognition and documentation. This is vital not only for Target 3 tracking and implementation, but to ensure private actors receive appropriate recognition and support for their role in tackling the biodiversity and climate crises.
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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 | 0.013 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".