Manitoba education reforms, white settler discourses, and the marginalization of Indigenous perspectives
Bibliographic record
Abstract
In 2019, the province of Manitoba started a process of reforming the education system, however it is important to question the role of white settler colonialism in this process. This critical discourse analysis examined how white settler colonialism is normalized and advanced through the discourses found in selected Manitoba education reform documents. Contrasting discourses emerged in the government documents and the briefs submitted from education organizations and school divisions. The dominant discourse, found particularly in the government documents and other documents, featured colour-blind ideology that normalized whiteness. Indigenous students were frequently discussed using a deficit narrative, while ideological discourse structures put distance between the Indigenous community and the education system. Neoliberal views of learning and achievement were emphasized in the dominant discourse, which conflicted with definitions of achievement put forth by Indigenous scholars. Attributes of Indigenous learning were often omitted or instrumentalized to further neoliberal views of learning and achievement. Superficial integration of Indigenous content and perspectives was evident, running counter to a more transformative trans-systemic integration of Indigenous and Eurocentric knowledge systems. In summary, these discourses worked to normalize and advance white settler colonialism and marginalize Indigenous perspectives, while contrasting discourses offered a transformative vision of an education system based in principles of equity.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.028 | 0.031 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".