Working towards decolonial futures in Canada: first steps for non-Indigenous fisheries researchers
Bibliographic record
Abstract
Motivated by the leadership, scholarship, and activism of Indigenous Peoples, there are growing calls to transform and decolonize Canadian institutions that govern fisheries research in Canada. As a predominantly non-Indigenous group that works at the intersection of fisheries and justice, we encounter questions daily about how to act as allies in these efforts and take up this urgent call in our own work. Our goal with this perspective is to synthesize and share some of what we have learned about encountering and combatting colonialism in the hope that it may offer something to other non-Indigenous and settler fisheries researchers who are grappling with colonization in their own work. This synthesis is based on both Indigenous scholarship and our own experiential learning. We look to actions fisheries researchers may take to advance Indigenous sovereignty in fisheries research. We offer this to our fellow non-Indigenous researchers who likely also struggle with similar questions, and hope that in doing so, we can help move towards decolonial fisheries futures.
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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 | 0.041 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.087 | 0.039 |
| Scholarly communication | 0.024 | 0.014 |
| Open science | 0.007 | 0.027 |
| Research integrity | 0.010 | 0.021 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".