Challenges in assessing the effects of environmental governance systems on conservation outcomes
Bibliographic record
Abstract
Effective governance is crucial for the success of conservation projects aimed at protecting wildlife populations and supporting human well-being. However, few large-scale, comprehensive syntheses have been conducted on the effects of different environmental governance types on conservation outcomes (i.e., biological and ecological effectiveness or effects of conservation on human well-being), and clarity on the quantity and quality of evidence remains dispersed and ambiguous. We attempted a systematic map of the evidence on the effectiveness of different governance types to meet desired conservation outcomes in Africa, Asia, and Latin America. However, early in this effort, we observed a general lack of empirical research on the links between governance and conservation outcomes. To fill observed data gaps in the evidence base, we tried triangulating governance data from alternative sources (Protected Planet database) and pooling evidence from research conducted within the same conservation areas. Limited data were contained in the Protected Planet database, and governance types in conservation areas and landscapes were complex, making it difficult to use these approaches to assign governance types to conservation areas. To illustrate our observations from the failed systematic map attempt, we prepared a rapid evidence map that outlines a subset of the evidence base of articles linking governance types and governance principles with conservation outcomes. Only 3.2% (34 of 1067) of the articles we screened directly related conservation outcomes to governance type, and even fewer related governance principles to conservation outcomes. Based on our findings, we recommend improving the evidence base by supporting empirical research and increasing the availability and quality of governance data in freely accessible databases. These recommendations are critical for enhancing understanding of the role of governance in conservation projects and improving conservation outcomes.
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.
How this classification was reachedexpand
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) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".