Navigating governance networks for community‐based conservation
Bibliographic record
Abstract
Governance networks can facilitate coordinated action and shared opportunities for learning among conservation scientists, policy makers, and communities. However, governance networks that link local, regional, and international actors just as often reflect social relationships and arrangements that can undermine conservation efforts, particularly those concerning community‐level priorities. Here, we identify three “waypoints” or navigational guides to help researchers and practitioners explore these networks, and to inspire them to consider in a more systematic manner the social rules and relationships that influence conservation outcomes. These waypoints encourage those engaged in community‐based conservation (CBC) to: (1) think about the networks in which they are embedded and the constellation of actors that influence conservation practice; (2) examine the values and interests of diverse actors in governance, and the implications of different perspectives for conservation; and (3) consider how the structure and dynamics of networks can reveal helpful insights for conservation efforts. The three waypoints we highlight synthesize an interdisciplinary literature on governance networks and provide key insights for conservation actors navigating the challenges of CBC at multiple scales and levels.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".