Recent advances in management strategy evaluation: introduction to the special issue “Under pressure: addressing fisheries challenges with Management Strategy Evaluation”
Bibliographic record
Abstract
Management strategy evaluation (MSE) is an increasingly popular tool for developing, testing, and implementing fisheries management regimes, oftentimes utilizing participatory modeling. This special issue, “Under pressure: addressing fisheries challenges with Management Strategy Evaluation”, includes eleven articles highlighting cutting edge MSE approaches and perspectives on improving stakeholder engagement. The special issue is the culmination of a two-session MSE symposium held during the 147th American Fisheries Society Annual Meeting in Tampa, Florida. We summarize the themes from the symposium and special issue articles. Contributions demonstrated that important strides have been made in quantifying and exploring risk (by including more sophisticated multispecies and socioeconomic components), developing and testing data-limited harvest control rules, acknowledging and diagnosing limitations of MSE (e.g., identifying exceptional circumstances), and dealing with issues of stakeholder engagement and dimensionality (e.g., determining appropriate representation, communication techniques, and participation levels). Although MSE is a not a panacea for marine policy and resource utilization issues, it is a useful tool for implementing co-management regimes that should become increasingly robust as the multidisciplinary nature of MSE processes continues to expand.
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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 | 0.040 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".