Governance and Conservation Effectiveness in Protected Areas and Indigenous and Locally Managed Areas
Bibliographic record
Abstract
Increased conservation action to protect more habitat and species is fueling a vigorous debate about the relative effectiveness of different sorts of protected areas. Here we review the literature that compares the effectiveness of protected areas managed by states and areas managed by Indigenous peoples and/or local communities. We argue that these can be hard comparisons to make. Robust comparative case studies are rare, and the epistemic communities producing them are fractured by language, discipline, and geography. Furthermore the distinction between these different forms of protection on the ground can be blurred. We also have to be careful about the value of this sort of comparison as the consequences of different forms of conservation for people and nonhuman nature are messy and diverse. Measures of effectiveness, moreover, focus on specific dimensions of conservation performance, which can omit other important dimensions. With these caveats, we report on findings observed by multiple study groups focusing on different regions and issues whose reports have been compiled into this article. There is a tendency in the data for community-based or co-managed governance arrangements to produce beneficial outcomes for people and nature. These arrangements are often accompanied by struggles between rural groups and powerful states. Findings are highly context specific and global generalizations have limited value.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.018 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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, 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".