Race, Indigenous knowledge, and a relational alternative in fisheries policy research
Bibliographic record
Abstract
This paper responds to the contemporary academic and policy interest in bridging, integrating, and co-producing knowledge across Indigenous and non-Indigenous difference. I draw on my work on fish and fish relations with Nipissing First Nation (NFN), a Nbisiing Anishinaabeg community that governs its fishing activities according to community-derived law. In the form of critical policy analysis, I respond to three core research questions driving a national research partnership on Indigenous and Western knowledge systems in fisheries governance: (1) How and to what extent are different knowledge systems incorporated into fisheries governance and processes by Indigenous nations in Canada at national, regional and local scales? (2) Can varied Indigenous knowledge systems (IKS) be used to improve the effectiveness of fisheries governance at national, regional, and local scales in Canada and internationally? (3) Can various IKS be used to inform and enhance an ecosystem-based approach to fisheries management in Canada and internationally, given the complexities of ecosystems and additional uncertainties posed by climate-induced changes? Indigenous knowledge certainly could and already does improve the effectiveness of fisheries governance in Canada, but this occurs despite the dominant resource regulatory regime. At Lake Nipissing, NFN leadership and an exceptional case of provincial recognition and support for Nbisiing Anishinaabeg law and jurisdiction have resulted in the recovery of the lake’s most sought-after fish population. Ultimately, I argue against “knowing” racialized forms of difference and conclude with an alternative, relational approach to fisheries policy and knowledge research. • It is necessary to question the assumptions and interests served by mapping discrete, binarized knowledge systems. • Instead of reify difference, leverage expanding interest in Indigenous knowledge by attending to relationship and process. • Respect for difference, complexity and limits makes the partial, emergent and relational visible, and coexistence possible. • Indigenous policy tools challenge Indigenous exclusion from dominant decision-making but do not call for more 'inclusion’. • Non-Indigenous society is called to harmonize laws and ways of being with Indigenous sovereignty and caretaking relations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".