Closing the feedback loop: on stakeholder participation in management strategy evaluation
Bibliographic record
Abstract
Management strategy evaluation (MSE) is a simulation-based approach to examine the efficacy of management options in achieving fishery-, ecosystem-, and socioeconomic-related objectives while integrating over system uncertainties. As a form of structured decision analysis, MSE is amenable to stakeholder involvement, which can reduce implementation barriers associated with nontransparent decision-making procedures. Based on analysis of three MSE processes (Atlantic tunas (Thunnus spp.), Atlantic herring (Clupea harengus), and eastern oysters (Crassostrea virginica)), we provide suggestions for improving stakeholder engagement in MSE. By assembling a workgroup and modeling team with diverse backgrounds, including professional facilitators, communication liaisons, and social scientists, dialogue can be improved and an atmosphere of mutual learning fostered. Communication further benefits from clearly defining roles, responsibilities, and terms of engagement for all involved; explicitly and transparently identifying goals and objectives of the MSE before modeling has begun; and, when appropriate, revisiting goals and objectives throughout the MSE process. Although MSEs are not without limitations, the participatory modeling framework, wherein stakeholders are actively engaged at each stage of MSE development, provides a useful mechanism to support fisheries management.
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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 | 0.433 | 0.518 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.011 | 0.019 |
| Scholarly communication | 0.017 | 0.023 |
| Open science | 0.005 | 0.035 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 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".