Governance and Conservation Effectiveness in Protected Areas and Indigenous and Locally Managed Areas
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it