Reading Between the Lines: Exploring Indigenous Anti-Racism through Vocabulary Choices in the Education Policies of Three Canadian Provinces from 2014- 2023
Bibliographic record
Abstract
Disparities in academic achievement between Indigenous and non-Indigenous students have existed for decades in Canada. This critical discourse analysis was conducted to determine the role of anti-racism in education policy in British Columbia, Saskatchewan, and Ontario and its impact on Indigenous learners. A review of the literature revealed the necessity of using an anti-racist education approach to guide the research. A critical discourse analysis (CDA) was conducted using a framework on discourse analysis along with guiding questions to direct the examination of the policy documents. The findings of the critical discourse analysis revealed that the provinces are in different places when embracing anti-racism in their educational policies. Secondary documents were analyzed for each province in addition to the sector plan policy documents which at times revealed a lack of alignment. The findings revealed commonalities between the provinces in a variety of areas: personal pronouns, shifts in vocabulary, Othering, collaborative partnerships, curriculum renewal, equity, diversity, inclusion, racism, and reconciliation. The examination of the themes revealed that the province of British Columbia is implementing various strategies to change the educational reality for all its students, with the focus being on equity and addressing issues of inequity. Saskatchewan and Ontario both have secondary documents that advocated for adopting an anti-racist lens, but their annual sector plan documents did not always mirror the same commitment to change. Recommendations include: exploring word choice, a focus on authentic collaboration, curriculum renewal and equity, and a responsibility for reconciliation. Educational transformation requires the adoption of an anti-racist lens, a duty to engage in reconciliation, valuing Indigenous voices, and a re-visioning of the educational systems that exist today. We need to make the commitment to engage in authentic relationships with Indigenous Peoples to create a better future for all our students.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.025 | 0.010 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".