Value- and ecosystem-based management approach: the Pacific herring fishery conflict
Bibliographic record
Abstract
An innovative value-and ecosystem-based management approach (VEBMA) is introduced that aims to expose policy tradeoffs, resolve resource conflicts, and foster ethical governance.VEBMA is applied to the Pacific herring Clupea pallasii fishery in British Columbia, Canada, which is mired in conflict between local and indigenous communities and the fishing industry over the management of herring, a forage fish with significant ecological, socioeconomic, and cultural value.VEBMA integrates an ecosystem-based approach (ecological modelling) with a valuebased approach (practical ethics) to examine the ecological viability, socioeconomic feasibility, and societal desirability of alternative fishery management scenarios.In the socioecosystem-based approach, we applied the Management Strategy Evaluation module within the Ecopath with Ecosim modelling framework to explore scenarios with harvest-control rules specified by various herring fishing mortalities and biomass cutoff thresholds.In the value-based approach, Haida Gwaii community and herring industry participants ranked a set of values and selected preferred scenarios and cutoff thresholds to open the fishery.The modelled ecological and socioeconomic impacts and risks and stakeholder preferences of the scenarios are synthesized in a deliberation and decision-support tool, the VEBMA science-policy table.VEBMA facilitates inclusive, transparent, and accountable decision-making among diverse stakeholders, such as local communities, industries, scientists, managers, and policy-makers.It promotes ethical governance in pluralistic societies via compromise, rather than consensus solutions to resolve 'wicked' problems at the science-policy interface.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".