<scp>T</scp>he last generation? Perspectives of inshore fish harvesters from Change Islands, Newfoundland
Bibliographic record
Abstract
Abstract Our investigation examines the perspectives of fish harvesters on key challenges facing the inshore fishery in Newfoundland and Labrador. The findings, based primarily on in‐depth interviews with harvesters in the town of Change Islands, show that fishers are deeply concerned about ineffective regulations, low prices for their catch, and rationalization policies. They explain how existing regulations restrict traditional cooperative fishing practices and impose rules that are not suited for local environmental conditions. Low prices for fish landed, they argue, are caused in part by lack of competition among buyers and a bonus system that favours larger enterprises. These conditions, combined with policies aimed at reducing the fishing fleet and barriers to youth involvement, threaten the long‐term survival of coastal communities. Overall, current policies keep inshore harvesters on the sidelines of an increasingly industrialized fishery. Local fish harvesters have valuable local, place‐based knowledge that can be used to develop more effective fishery management policies and marketing strategies, and in this article we share their recommendations on how to build more sustainable fisheries. However, traditional fishing communities—along with the potential social, cultural, economic, and environmental benefits of smaller‐scale, community‐based fishing—need to become more visible for these changes to occur.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".