Onshore benefits from fishing: Tracking value from the northern shrimp fishery to communities in Newfoundland and Labrador
Bibliographic record
Abstract
Most fisheries assessments focus on biological and ecological conditions, fishery impacts and performance. Economic and social conditions and outcomes, however, are rarely explicitly tracked or evaluated. Using data from the Canadian northern shrimp inshore fleet, from employment statistics, and from Newfoundland and Labrador municipal budgets, this paper examines links between harvesting and post-harvesting economic activities and municipal infrastructure and services within adjacent onshore communities. The broad geographic distribution of home ports and landing destinations resulted in extensive economic ripple effects in areas such as food retail, shipyard maintenance and fuel services, which amounted to almost $9,000,000 in onshore expenditures distributed among 15 landing ports in 2014. Additionally, because tax payments from shrimp processing plants impact municipal budgets and services, the findings show that community-level benefits can be tracked and measured, with the implication that fisheries management objectives, such as supporting adjacent communities, are also achievable and measurable. While the impacts from a recent decline of the northern shrimp inshore fishery are stark for adjacent communities, the two decades of substantial contributions from this fishery were made possible because policy decisions at both the provincial and the federal level were explicitly developed to support fishing communities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".