Trust and Influence in the Gulf of Mexico’s Fishery Public Management Network
Bibliographic record
Abstract
Sustainable fishery management is a complex multi-sectoral challenge requiring substantial interagency coordination, collaboration, and knowledge sharing. While scholars of public management network theory and natural resource management have identified trust as one of the key ideational network properties that facilitates such interaction, relatively few studies have operationalized and measured the multiple dimensions of trust and their influence on collaboration. This article presents the results of an exploratory study examining the Gulf of Mexico fishery management network comprised of more than 30 stakeholder organizations. Using an empirically validated survey instrument, the distribution of four types of trust, three gradations of influence, and the degree of formality and informality in actor communications were assessed across the fishery public management network. The analysis reveals generally low levels of interorganizational procedural trust and a high degree of network fragmentation along the international border. Civil servants based at U.S. organizations reported nearly no interactions with Mexican agencies, and vice versa. Rational (calculative) trust was the most important in bringing about reported change in other organizations, while dispositional distrust and affinitive (relational) trust also had significant effects. The results suggest that, although transactional interorganizational relationships prevail in Gulf of Mexico fishery governance, well-developed professional relationships contribute meaningfully to the reported success of public fishery network management and warrants further policy attention in order to help ensure sustainability.
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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 | 0.012 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".