Some Back-Ended Legal and Political Issues of United States Fisheries Management
Bibliographic record
Abstract
In response to over-exploitation and ecosystem degradation, United States federal fisheries policy is shifting from species-based to ecosystem-based management. In addition, the reauthorized Magnuson-Stevens Fisheries Conservation and Management Reauthorization Act of 2006 identifies the following goals to be achieved by 2011: end over-fishing, create market-based incentives, strengthen enforcement mechanisms, and improve cooperative conservation efforts. We refer to these goals (including the “status quo”) as front-ended policy objectives. Left unresolved are what we term back-ended policy and legal issues, specifically including issues involving the legal limitations that inhibit full consideration of ecosystem-based management principles through the adopting of scientific information. In this paper, we identify and examine some of these legal limitations, including the standard of review used in judicial proceedings. In addition, we also suggest some potential solutions to these major governance obstacles. We believe the ultimate value of this paper is the identification of recurring framework issues in United States fisheries management if, left unresolved, will continually limit the conservation-related goals such as those identified in the Magnuson-Stevens Fisheries Conservation and Management Reauthorization Act of 2006. As such, these legal obstacles should be a primary focus of policy makers who wish to achieve fishery conservation goals in-line with scientific research.
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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 | 0.031 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.021 |
| Scholarly communication | 0.021 | 0.012 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.015 | 0.015 |
| Insufficient payload (model declined to judge) | 0.002 | 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".