Identifying stakeholder preferences for rebuilding a Canadian Atlantic redfish fishery—limitations and benefits of different opinion survey approaches
Bibliographic record
Abstract
Fisheries management authorities seek to improve the incorporation of stakeholders’ preferences into decision-making but conventional approaches to assessing stakeholder viewpoints may risk under-representing a diversity of opinions. In Atlantic Canada's Units 1 and 2 redfish fisheries, there are competing visions about re-developing the fishery following historical overfishing. A management strategy evaluation (MSE) sought to identify which fishery objectives should guide the formulation of performance metrics. Following the MSE, we carried out a study to further sample the social, economic, and ecological objectives for the fishery using multiple questioning methods, i.e., workshops, questionnaires, and interviews. Results of interviews and questionnaires identified areas of consensus and complexity of opinion among the different groups (commercial, government, and Indigenous), and showed that the workshop-based performance metrics defined in the MSE underrepresented the diversity of stakeholder preferences, particularly regarding social and economic goals. Multi-method and multi-disciplinary approaches to formalizing objectives are resource-intensive. However, there is value in applying multiple methods to systematically develop and formalize performance metrics that accurately reflect a diversity of stakeholders’ priorities for the fishery.
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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 | 0.074 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".