Paper Fish and Policy Conflict: Catch Shares and Ecosystem-Based Management in Maine's Groundfishery
Bibliographic record
Abstract
The National Oceanic and Atmospheric Administration professes support for ecosystembased fisheries management, as mandated by Congress in the Fishery Conservation and Management Act, and as endorsed by the Obama Administration's national ocean policy.Nonetheless, driving agency policies, including catch shares and fishing quotas, focus principally on individual species, diverting attention from ecosystem considerations such as habitat, migratory patterns, trophic relationships, fishing gear, and firmlevel decision making.Environmental non-governmental organization (ENGO) agendas manifest similar inconsistencies.A case study of Maine's groundfishery demonstrates implications of this policy conflict at the local level.There, multigenerational fishing villages have historically pursued diversified and adaptive livelihood strategies, supported by local ecological knowledge.This tradition is increasingly eroded by regulatory constraints, including catch shares.Field observation, interviews, survey data, and archival review reveal that industry-supported, ecosystem-focused proposals have been rejected by the New England Fishery Management Council, despite the apparent failure of single-species approaches to sustain fish populations, fished ecosystems, and fishing-dependent communities.The creation of groundfishery catch share sectors is likely to perpetuate industry consolidation and political entrenchment under more mobile capital, following precedent set by days-at-sea, and making area protections and gear restrictions less likely.Pending marine spatial planning efforts could enhance social-ecological resilience by creating new opportunities for transdisciplinary decision support, and broader public participation and accountability.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 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".