Fishing for justice : an ethical framework for fisheries policies in Canada
Bibliographic record
Abstract
Canadian fisheries are in crisis. On both the Pacific and Atlantic coasts, stories abound of fisheries closures or failures and coastal communities in difficulty. A new approach to fisheries policy is required, one which recognises the intrinsic value of all participants in the fisheries ecosystem and is capable of providing guidance on how to make policy decisions. The principles of environmental ethics provide a framework for developing justice-based fisheries policies. The environmental ethics literature is first explored, with special attention to fisheries issues. From this review, a justice-based framework is identified, in which five types of justice are viewed as pertinent to fisheries concerns. This framework is then translated into an assessment tool, based upon the Rapfish method for rapid appraisal of fisheries and using a set of justice-based ethical criteria. These criteria are evaluated and, through a paired comparison survey, further explored. An assessment of a range of Canadian marine fisheries is conducted using these ethical criteria. Subsequently, a modified Rapfish assessment, using the original criteria supplemented with additional customised criteria, is conducted for Aboriginal fisheries for Pacific salmon in British Columbia. Additionally, a study is conducted which explores preferences regarding the abundance and diversity of fisheries ecosystems. Finally, the commercial fishery for Pacific salmon in British Columbia is presented as a case study. The Rapfish assessment results are presented, and considerations as to how to operationalise just policies for this fishery are suggested. Recommendations include: balancing the composition of the commercial fleet, based upon ecological impacts of the various gear types; encouragement of local stewardship and community involvement; and inclusion of various forms of knowledge in fisheries management and decision-making.
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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 | 0.030 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.044 | 0.034 |
| Scholarly communication | 0.029 | 0.008 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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".