Net loss: A cost‐benefit analysis of the Canadian Pacific salmon fishery
Bibliographic record
Abstract
This article applies cost-benefit analysis to the Canadian Pacific commercial salmon fishery. It demonstrates that government policies to preserve the fishery have resulted in higher net social costs than would have resulted from a "do nothing" policy, notwithstanding the rent dissipation associated with unconstrained resource exploitation. The value of landings and the private costs of the harvest over a cycle (1988-1994) are calculated. On average, fishers extracted rents of C$34.7 million (in constant 1995 Canadian dollars) annually. The public costs of enhancing the resource and organizing and policing the harvest are estimated. When these costs are included in the calculation, net benefits drop to an average of negative C$55.6 million annually. This translates into a net present value (NPV) of the salmon fishery of negative C$784. The effects on NPV of both modest policy changes implemented in 1996-1997 and of a more dramatic but credible fleet rationalization program are provided. The results indicate that further policy change is called for. More generally, the study shows that policy reform that would significantly benefit both the private sector (through reduced rent dissipation) and the public sector (through reduced government expenditures) can be surprisingly difficult. © 2000 by the Association for Public Policy Analysis and Management.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".