Impacts of fishery policy on the distribution of access and community benefits
Bibliographic record
Abstract
This paper provides an illustration of how fishery policies, particularly relating to transferability of licenses and quota holdings, can alter the distribution of fishery access, rights and benefits across fishing communities. Such distributional changes have consequences, but these are rarely made explicit within management, and there is too often a lack of effort to predict, monitor or mitigate those consequences. As seen in this paper, the lack of attention to distributional impacts can produce possibly-unintended but certainly highly negative impacts on some fishing communities. In particular, a temporal analysis of the distribution of access and benefits for lobster, groundfish and herring fisheries in part of the Atlantic region of Canada shows how a specific community (Grand Manan) lost fishery access and benefits, linked to policy changes over a four-decade time period. The resulting community-level impacts, including a decline in local prosperity, and a loss of diversity and resilience in the local economy, were largely untracked by government. This experience demonstrates the importance of establishing and monitoring explicit objectives related to community viability and wellbeing, within fishery management and policy processes. Greater attention to the distributional consequences of fisheries policies (and regulations) can contribute to policy that better balances multiple management objectives and trade-offs among these, and that can better consider concerns regarding fairness and equity.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".