Competing use of marine space in a modernizing fishery: salmon farming meets lobster fishing on the Bay of Fundy
Bibliographic record
Abstract
Coastal fishing communities are frequently portrayed as bastions of tradition at odds with the modernizing forces of technological change and industrial capitalism. This article examines this debate in the context of intensive aquaculture introduced into regions formerly dependent on the wild fishery, specifically with respect to the explosive growth of salmon farming in New Brunswick. New farm sites are large, often located within or close to traditional lobster fishing areas, which has motivated considerable opposition from local fishermen. This article presents findings from research on interactions between salmon farming and lobster fishing around Deer Island and Grand Manan, New Brunswick. Fishermen and salmon farmers are concerned about possible long‐term effects of farm operations on marine environmental quality and lobster health, and many are concerned about the concentration of ownership and lack of local control over the aquaculture industry. The potential for physical displacement from traditional fishing grounds is real, but the actual impacts have been tempered by a combination of factors, including unusually large lobster catches in recent years; technological advances that have encouraged a shift in lobster fishing effort further offshore, away from salmon farm sites; and social accommodations between salmon site managers and those who 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".