Ludwig?s Ratchet and the Collapse of New England Groundfish Stocks
Bibliographic record
Abstract
The stocks of principal groundfish species off New England have collapsed, creating economic hardship and dislocation in fishing communities from Rhode Island to Maine. In this article we analyze the causes of this collapse using the ?ratchet effect? described by Ludwig, Hilborn, and Walters (1993) as a framework. According to Ludwig, Hilborn, and Walters, powerful economic and political interests drive fisheries to overcapitalize and overexploit despite scientific evidence that stocks are declining. When the fishery is no longer economically viable, governments provide financial assistance to minimize economic hardship. When stocks increase there is another rush to invest, and the cycle repeats itself. The history of groundfish management in New England conforms well to this model. Optimism among fishers and government over U.S. control of this fishery in 1977 stimulated successive rounds of investment that built up excessive fishing capacity despite warnings from scientists that stocks were becoming weaker. Management regimes designed by the New England Fishery Management Council were ineffective in constraining fishing effort. Collapse of the stocks has led to severe restrictions on fishing and to government assistance. We suggest that the integration of science, management, and harvesting sectors through ecosystem-based management offers the best means of avoiding similar situations in the future.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".