Toward an Indigenist Ecology of Knowledges for Canadian Literary Studies
Bibliographic record
Abstract
Critics such as Marie Battiste, Lee Maracle, Sakej Henderson, and Lewis Gordon have called attention to how knowledge was and is a central target of colonial domination, as well as to how the other side of genocide is epistemicide. With this troubling history of “cognitive imperialism” (Gordon) in mind, Boaventura de Sousa Santos, Joao Arriscado Nunes, and Maria Paula Meneses insist that “there is no global social justice without global cognitive justice” and the “monoculture of [Western] scientific knowledge” must be replaced with an “ecology of knowledges.” For such a critical approach to be developed in a way that would be relevant for Canadian literary criticism, and to contribute to an ethical space of study, the genealogies underpinning Eurocentric knowledge systems must be questioned, and the kinds of Indigenous knowledge that have been suppressed and dismissed through them must be reconsidered.
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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 | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.017 | 0.011 |
| Science and technology studies | 0.055 | 0.107 |
| Scholarly communication | 0.039 | 0.014 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 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".