Ways in which Indigenous People’s History in Canada Evince Epistemic Injustice and Resistance
Bibliographic record
Abstract
When it comes to Western academia, Indigenous Peoples around the world have been and continue to be marginalized, especially in terms of representation within the literature. Therefore, this research paper will help remedy this issue by exploring epistemic injustice and the way in which epistemic injustice has and continues to harm Canada’s Indigenous Peoples. It will highlight four different lenses brought forward by Gaile Pohlhaus, Jr. as her framework allows for the representation of many different forms of epistemic injustice, while also acknowledging that no one approach is absolute. Resistance to epistemic injustice by the Indigenous Peoples will be addressed in the form of Indigenous literature and the Truth and Reconciliation Commission’s Calls to Action. Canada asserts that they are on a path toward truth and reconciliation, and so it is therefore important to highlight and address these harms so we can move forward with reconciliation and healing.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.054 | 0.046 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".