Overfishing in Canada and the United States: A Comparative Study of Policy and Legislation
Bibliographic record
Abstract
The purpose of this Capstone project was to first explore the causes of overfishing, then to explore how the issue is both viewed and dealt with in terms of policy and legislation in two highly comparable jurisdictions: Canada and the United States. The research draws to attention that there is a distinct lack of effective coastal commercial fisheries management in Canada and when compared to the United States, the degree of mismanagement becomes even more apparent. It is discovered that despite the magnitude of the collapse of the Atlantic cod fishery in Newfoundland, Canada has still yet to develop sufficient policy and legislation to effectively combat the ongoing issue of overfishing in coastal Canadian waters. This is sharply contrasted by U.S. fisheries management under the federal Magnuson-‐ Stevens Act that by nearly all accounts has been monumental in the country’s progress in controlling overfishing. Researching the causes of overfishing and comparing fisheries policy and legislation in both countries gleans the conclusion that not only can overfishing issues be solved through strong federal fisheries management, but the United States is currently doing so through effective legislation that Canada desperately requires. Furthermore, it is recommended that Canada should achieve stronger federal fisheries management through the legislative measure of amending the federal Fisheries Act to not only recognize overfishing but to include rebuilding plans for overfished stocks. The recommended rebuilding plans are inspired by the stock rebuilding measures laid out in the United States’ federal Magnuson-‐Stevens Act.
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) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.007 | 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".