Refracting the State Through Human-Fish Relations: Fishing, Indigenous Legal Orders and Colonialism in North/Western Canada
Bibliographic record
Abstract
This piece explores how human-fish relations in a) Paulatuuq, NWT in arctic Canada and b) amiskwaciwâskahikan (Edmonton, Alberta, Canada) in Treaty Six Territory act as a ‘micro-site’ where Indigenous peoples have negotiated, and continue to negotiate, concurrent and often contradictory ‘sameness and difference’ vis-a-vis the State and its ideologies about lands, waters and the more-than-human in order to assert and mobilize imperatives of reciprocity, care and tenderness towards fish as more-than-human beings. I put forth a theory of fish ‘refraction’ and dispersion, which is a process through which Indigenous peoples in Paulatuuq and amiskwaciwâskahikan bend and disperse state laws and norms through local relations to fish and waters. Exploring the ways that humans and fish alike work to navigate the complexities and paradoxes of colonialism in Alberta and the Northwest Territories in the past and present, I theorize a fishy and watery form of refraction of state laws, imperatives and colonial paradigms by Indigenous peoples in Canada. In a time of rapid fish decline across the country --which some argued is tied to the global realities of the Sixth Mass Extinction Event-- I argue for the urgency and necessity of centering human-fish relations, alongside other fleshy engagements, in contemporary and future political struggles.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.030 | 0.023 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".