Examining Canadian governance of First Nations fisheries along the coast of British Columbia through a theoretical lens of food justice
Bibliographic record
Abstract
Indigenous peoples living along the coast of what has become known as British Columbia hold longstanding relationships with the fish who share their waters, forged, and maintained through practices guided by enduring protocols. The following dissertation uses a theoretical lens of food justice to attend to the ways fisheries management, as enacted by Canada, may be interrupting relationships of Indigenous fisheries, and limiting real opportunities for resisting power structures. The diverse efforts of First Nations to reassert Indigenous governance of fisheries are recognised as central to realizing food justice. Scholars of fisheries and food studies are invited to examine how their research can support such efforts. Chapter 2 explores the Doctrine of Priority—the priority that Aboriginal and treaty rights to fish must be granted in decision making—as it has been interpreted by Canadian courts through an analysis of case law documents. Courts are found to privilege a dematerialized understanding of rights out of step with contemporary theories of rights, cultural continuity, and justice. Chapter 3 models restorative diets for coastal First Nations—diets accessible under the interdependent conditions of Indigenous governance reassertion and ecological restoration. I demonstrate a consistent and sizeable gap between harvest rates suggested by the types of evidence currently being used, which reflect already disrupted diets and practices, and harvest rates to support restorative diets. Chapter 4 analyses responses to the Nuxalk Food Resilience Survey to characterise household food resilience among Nuxalkmc across multiple criteria and to consider the extent to which criteria of household food resilience converge, or not. I find that there are “pockets” of household food resilience that might bolster the resilience of the broader Nuxalk food system given favourable governance conditions. Chapter 5 argues that evaluating the potential effects of fisheries management strategies on capabilities, or what people can do with what they have, contributes to the purpose of holding governance bodies accountable for the effects their decisions have on well-being and to promoting more just fisheries. I support my arguments by bringing together thinking from well-being scholars and empirical examples from fisheries studies.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.025 | 0.009 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".