Understanding the Role and Challenges for Indigenous and Community-Governed Lands in Contributing to Target 3 of the Global Biodiversity Framework
Bibliographic record
Abstract
Target 3 of the Kunming-Montreal Global Biodiversity Framework commits nations to protecting and conserving at least 30% of the world’s terrestrial and inland water areas and coastal and marine areas by 2030 (30 × 30). There can be significant overlap with Indigenous and traditional territories (ITTs) and protected areas. We explore if and/or how ITTs are currently recognized and reported as contributors to national protection targets by analyzing whether these territories are counted as standalone conservation areas, integrated into government-led protected and conserved area networks or systems, or neither, in 18 countries. Our analysis reveals critical linkages between tenure regimes, ITTs and their recognition in reporting to global area-based conservation databases. Legal recognition of tenure rights, particularly ownership and stewardship rights, emerged as the strongest predictor of whether ITTs are formally being accounted for in these databases. Our findings also reveal that the contribution of ITTs to national protection targets not only depend on tenure type but also on governance rights, despite the way it is reported. We categorize systemic barriers and opportunities that have implications for the contribution of ITTs to 30 × 30 goals.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | medium |
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
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".