A framework for investigating commercial license and quota holdings in an era of fisheries consolidation, concentration and financialization
Bibliographic record
Abstract
This paper proposes a novel framework to conceptualize and investigate license and quota holdings within fisheries jurisdictions and regions. We motivate and develop the framework by drawing together two bodies of literature that have been relatively disparate: one on fisheries industrialization and the other, from food studies and food systems scholars, on consolidation and concentration in farming and agrifood value chains. An important observation from food studies and food systems research is that a spectrum of logics -- from productive through to speculative -- motivate farmland accumulation and shape patterns of ownership, investment, and power in farming regions. We apply the framework to investigate license holding portfolios within the federally-managed fisheries jurisdiction off of Canada’s western-most province, British Columbia (BC). We calculate the market value of a selection of large portfolios. We also begin to explore power dynamics, finding that those with large and valuable portfolios are uniquely positioned to exercise and expand control within the jurisdiction. Fisheries and marine policy researchers should assume that hedge funds, private investment and equity firms, and other publicly traded businesses now regularly assess the investment potential of licenses and quota; case-based, comparative, and policy-oriented research are all urgently needed.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".