Fish pluralities: Human-animal relations and sites of engagement in Paulatuuq, Arctic Canada
Bibliographic record
Abstract
This article explores human-fish relations as an under-theorized “active site of engagement” in northern Canada. It examines two case studies that demonstrate how the Inuvialuit of Paulatuuq employ “fish pluralities” (multiple ways of knowing and defining fish) to negotiate the complex and dynamic pressures faced by humans, animals, and the environment in contemporary Arctic Canada. I argue that it is instructive for all Canadians to understand the central role of humans and animals, together, as active agents in political and colonial processes in northern Canada. By examining human-fish relationships, as they have unfolded in Paulatuuq over the last 50 years, we may develop a more nuanced understanding of the dynamic strategies that northern Indigenous people, including the Paulatuuqmiut (people from Paulatuuq), use to navigate shifting environmental, political, legal, social, cultural, and economic realities in Canada’s North. This article thus places fish and people, together, as central actors in the political landscape of northern Canada. I also hypothesize a relational framework for Indigenous-State reconciliation discourses in Canada today. This framework expands southern political and philosophical horizons beyond the human and toward a broader societal acknowledgement of complex and dynamic relationships between people, fish, and the land in Paulatuuq.
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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 | 0.003 | 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 | 0.041 | 0.020 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".