The role of boundary organizations in co-management: examining the politics of knowledge integration in a marine protected area in Belize
Bibliographic record
Abstract
Marine protected areas (MPAs) are an increasingly popular tool for management of the marine commons. Effective governance is essential if MPAs are to achieve their objectives, yet many MPAs face conflicts and governance challenges, including lack of trust and knowledge integration between fishers, scientists, and policy makers. This paper considers the role of a boundary organization in facilitating knowledge integration in a co-managed MPA, the Gladden Spit and Silk Cayes Marine Reserve in Belize. Boundary organizations can play an important role in resource management, by bridging the science-policy divide, facilitating knowledge integration, and enabling communication in conditions of uncertainty. Drawing on ethnographic research conducted in Belize, the paper identifies four challenges for knowledge integration. First, actors have divergent perspectives on whether and how knowledge is being integrated. Second, actors disagree on resource conditions within the MPA and how these should be understood. Third, in order to maintain accountability with multiple actors, including fishers, government, and funders, the boundary organization has promoted the importance of different types of knowledge for different purposes (science and fishers’ knowledge), rather than the integration of these. Finally, a lack of trust and uneven power relations make it difficult to separate knowledge claims from political claims. However, even if knowledge integration proves difficult, boundary organizations may still play an important role by maintaining accountability, providing space for conflicting understandings to co-exist, and ultimately for governance institutions to evolve.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.021 | 0.022 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| 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".