Buyback programs in commercial fisheries: effciency versus transfers
Bibliographic record
Abstract
A Cournot model of investment is used to characterize the pre‐ and post‐buyback investment equilibrium for vessels operating in a total‐allowable‐catch‐regulated fishery. Welfare effects – the net welfare gains or losses and the distributional effects – that may be expected from vessel buyback programs are identifed. Net welfare effects depend on the ability of remaining vessels to replace buyback capital, the speed of capital replacement, and capital investment irreversibility. Net welfare effects are likely to be positive only under exceptional technological and capital‐market conditions. A brief review of the British Columbia Pacific Salmon Revitalization Plan is presented to anchor the theoretical model. JEL Classification: Q28, D24 Les auteurs utilisent un modèle d;qgainvestissement à la Cournot pour analyser la situation d;qga´equilibre d'investissement avant et après le rachat de vaisseaux opérant dans des pêches où le total des prises permises est réglementé. Les effets de bien‐être – les effets nets de gains ou de pertes et les effets de redistribution – qu;qgaon peut anticiper de programmes de rachats de vaisseaux sont identifiés Les effets nets de bien‐être dépendent de la capacité des vaisseaux qui restent en service à remplacer le capital racheté, de la vitesse avec laquelle le capital est remplacé, et de l'irréversibilité de la capitalisation. Les effets nets de bien‐être sont susceptibles d'être positifs seulement dans des conditions exceptionnelles sur le plan technologique et pour ce qui est du marché du capital. Les auteurs examinent le plan de revitalisation du saumon du Pacifique de la Colombie Britannique pour donner un point d;qgaancrage au modèle théorique.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".