Bibliographic record
Abstract
Abstract We examine the claim in Christensen that Maximum Economic Yield (MEY) is equal to Maximum Sustainable Yield (MSY). The basis for this claim is that MEY considers only the ‘catching’ of fish and that when the full value‐chain is considered; it is the MSY level that maximizes economic value. We argue that to maximize society’s benefit from a given sector of an economy, resources need to be allocated across all sectors such that additional net benefits from employing one more unit of society’s resources are equalized across all sectors of the economy. In this way, the opportunity cost of employing society’s resources across all economic sectors is minimized. In an economy where all resources are fully utilized, further value added in the value chain for fish is an additional cost and has the effect of reducing fishing effort and optimum yield rather than the opposite. In a less developed economy or a developed one in recession where all resources are not fully used, the multiplier effect could be important, and if it is high for fisheries it would be an argument to maximize sustainable yield and effort. We show, using current input‐output data, that this is not the case. Furthermore, from a simple principle of optimization, we know that to optimize a sector that consists of many segments through time, one has to optimize every portion of the chain through time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".