How do commercial fishing licences relate to access?
Bibliographic record
Abstract
Abstract Commercial fishing licences are central to fisheries management systems. They define and allocate harvest rights, place rules upon authorized harvesters and, in some cases, require holders to pay user fees. In this paper, we ask how licences and licensing relate to access, itself a broader concept defined as the opportunity to derive benefits from resources and that draws attention to how institutions and social structures enable and constrain different individuals and groups. Using published literature, reports and publicly available licence data for fisheries off of British Columbia, Canada, we overview licensing history and examine all major commercial licence types in the jurisdiction. Using a network approach, we also describe the diverse suite of licence portfolios held in 2017. Results show that there were 6,563 commercial fishing licences registered by 2,377 unique holders, including a handful that hold ‘access‐rich’ and a much larger number who hold ‘access‐constrained’ portfolios. The literature review and analysis support two broadly applicable conclusions. First, that licensing history shapes access and that limited entry policies continue to influence who benefits from fisheries resources well beyond implementation. Second, that analysing licence holdings suggests business strategies and fishing prospects available to different harvesters and other commercial fisheries participants in a jurisdiction. In response to demand for greater attention to human dimensions and to the perception that indicators are challenging to develop and integrate, we advance conceptual thinking and practical approaches relevant to fisheries research and evaluation.
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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 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".