Epistemic injustice in a settler nation: Canada’s history of erasing, silencing, marginalizing
Bibliographic record
Abstract
This paper examines an application of epistemic injustice not fully explored in the literature. How does epistemic injustice function in broader contexts of relationships within countries between colonizers and colonized? More specifically, what can be learned about the ongoing structural aspects of hermeneutical injustice in Canada’s settler history of the forced assimilation of Indigenous peoples and the resultant erasing and marginalizing of Indigenous histories, languages, laws, traditions, and practices? In this paper, I use insights from Canada’s Truth and Reconciliation Commission report to challenge dominant understandings of reconciliation, reciprocity, respect for agency, and the rule of law in settler nations. In its retrieval of the richness and diversity of Indigenous collective interpretive resources, both past and present, Canada’s Truth and Reconciliation Commission draws on a broad and full account of relationships that have shaped Indigenous lives and communities, non-Indigenous lives and communities, the interactions of Indigenous and non-Indigenous peoples and communities, and the relationships of all of these to and through the state.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.062 | 0.034 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 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".