Bridging Organizations Drive Effective Governance Outcomes for Conservation of Indonesia’s Marine Systems
Bibliographic record
Abstract
This study empirically investigates the influence of bridging organizations on governance outcomes for marine conservation in Indonesia. Conservation challenges require ways of governing that are collaborative and adaptive across boundaries, and where conservation actions are better coordinated, information flows improved, and knowledge better integrated and mobilized. We combine quantitative social network analysis and qualitative data to analyze bridging organizations and their networks, and to understand their contributions and constraints in two case studies in Bali, Indonesia. The analysis shows 1) bridging organizations help to navigate the 'messiness' inherent in conservation settings by compensating for sparse linkages, 2) the particular structure and function of bridging organizations influence governing processes (i.e., collaboration, knowledge sharing) and subsequent conservation outcomes, 3) 'bridging' is accomplished using different strategies and platforms for collaboration and social learning, and 4) bridging organizations enhance flexibility to adjust to changing marine conservation contexts and needs. Understanding the organizations that occupy bridging positions, and how they utilize their positionality in a governance network is emerging as an important determinant of successful conservation outcomes. Our findings contribute to a relatively new body of literature on bridging organizations in marine conservation contexts, and add needed empirical investigation into their value to governance and conservation in Coral Triangle nations and beyond.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".