The sound management of a fishery as a social engineering: applying Karl Popper's demarcation criterion to an Area 2 stock of Pacific halibut
Bibliographic record
Abstract
The Newfoundland fishery for Atlantic cod was once the largest cod fishery in the world. In the early 1990s this fishery formed part of an Atlantic Canadian groundfish fishery collapse that has become one of the world’s most prominent case studies of failure in fisheries management. The proposition to be advanced in this paper is that this fishery collapse is attributable to the use of unsound inductive arguments that were over-reliant on ‘facts’ or data. Under Karl Popper’s non-inductive theory of method the ability to understand and avoid a fishery collapse is not dependent on the certainty of the ‘facts’ or data, it is dependent of the soundness of the decisions that are taken. What is, or is not, a sound decision or sound argument is not a distinction discoverable ‘naturalistically’ by empirical science; rather, the distinction is based in logic. Sound management decisions require a critical or falsifiable view of science that has to be ‘demarcated’ from a verifiable and inductive view, two views illustrated in this paper by a singular 47 year data set of Pacific halibut. It is my prescriptive thesis that if the World’s commercial fisheries are to realize a long-term sustainability they will need to be managed under a critical or falsifiable view of fishery science in which a trial and error management is guided by rules of thumb with prior improbability. After all, Canada’s inshore Maritime lobster fishery has been managed in this way for well over a century without collapse.
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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 | 0.017 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".