Distinguishing input controls from ouput controls in Atlantic Canada's fisheries: explaining the decline and collapse of Newfoundland's Atlantic cod stocks
Bibliographic record
Abstract
The lobster and groundfish fisheries of Atlantic Canada have been managed in very different ways. The Atlantic lobster fishery has been managed by input controls in which regulations have been developed by a posteriori deductive argument to control the intensity of the gear used to catch lobsters. By contrast the Atlantic groundfish fisheries have been managed by output controls involving the a priori inductive arguments of stock assessment in which limits are put on the amount of groundfish coming out of a fishery. Karl Popper excludes induction from his theory of method since induction leads to logical inconsistencies such as a ‘scientific’ ethics (i.e. the notion that science can on its own tell us what should be done), a fisheries example of which is the use of reference points and harvest guidelines in an attempt to guide the normative use of data. It is my thesis that the prejudicial nature of a fish stock assessment with its embedded monism of ‘scientific’ ethics is to be held responsible for the overfishing and collapse of Atlantic goundfish fisheries including Newfoundland’s Atlantic cod stocks. If Atlantic Canada’s groundfish fisheries are to be managed by sound and rational decisions, they will have to join the Atlantic lobster fishery as a well regulated institution capable of controlling the levels of effort used to catch fish.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".