Fishing for common ground : broadening the definition of 'rights-based' fisheries management in Iceland's Westfjords
Bibliographic record
Abstract
Since the 1980s, so-called “rights-based” fisheries management regimes – specifically those designed to apply market forces to problems of inefficiency and overfishing by divvying up fixed, tradable proportions of a total allowable catch among individuals or cooperatives – have become both one of the most widely advocated and most contentious aspects of marine resource management. Iceland, promoted by some as a successful international model of this approach, has been the site – for nearly thirty years – of fierce debate and controversy regarding the system’s effects on regional development, social justice and wealth disparity. This thesis uses a phenomenological approach, a set of ten semi-structured interviews, and document analysis to explore how inhabitants of the Icelandic Westfjords understand and articulate their ‘rights’ in the context of fisheries management, particularly in the wake of the 2008 financial crisis. Respondents discuss a range of perceived ‘rights’ not usually considered in management design, most notably the right to ‘fate-control’. Discussions of rights-based management in the Westfjords reflect deeper concerns regarding identity, fear of change, democratic values and a sense of personal agency.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.024 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".