‘Consent’ as epistemic recognition: Indigenous knowledges, Canadian impact assessment, and the colonial liberal democratic order
Bibliographic record
Abstract
and the inclusion of Indigenous peoples and knowledges in impact assessment. We situate the logic within the politics of recognition in Canada-a politics that aims to shore up national unity in the face of regular challenges to it. We use the Canadian results from a recent scoping review on conceptions of environmental justice in impact assessment to highlight the challenges of invoking recognition, and we provide a theoretical analysis of these challenges. To do this, we highlight the ways in which 'we-making' is 'knowledge-making' and 'knowledge-making' is 'we-making'. In this sense, recognizing Indigenous knowledges is part of Canada's answer to the challenge of constructing and stabilizing a political 'we': a community of political subjects with shared connection to a nation state via the institutional, social, and cultural apparatuses that generate the kind of publicly visible legal and technical knowledge upon which the state's authority depends. We show how this project relies on actively obscuring the relationship between 'we-making' and 'knowledge-making' by treating 'knowledge-making' as neutral and un-situated, putting into practice a universalist logic. This logic shores up power because obscuring the situatedness of dominant knowledges also obscures the situatedness of the dominant political orders with which they are intertwined. We ultimately argue that Canada's approach to recognizing Indigenous knowledges helps consolidate power by sidestepping ongoing jurisdictional struggles with Indigenous peoples.
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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 | 0.029 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.025 | 0.111 |
| Scholarly communication | 0.021 | 0.013 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.005 | 0.007 |
| 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".