A critical discourse analysis of Manitoba’s K-8 social studies framework of outcomes
Bibliographic record
Abstract
Many provinces across Canada, including Manitoba, are undergoing reforms to their educational structures and systems. Included in these reforms is a mandate to introduce sweeping curricular changes which will impact which particular societal values are conveyed as well as the ways in which teachers will engage students with particular curricular content knowledge. Given the current Canadian context, it is crucial that proposed curricular reforms reflect the Calls to Action presented by Canada’s Truth and Reconciliation Commission (2015), findings from the National Inquiry into Missing and Murdered Indigenous Women and Girls (2016), as well as anti-racism education. It is with this context that I have conducted a critical discourse analysis (CDA) of Manitoba’s Kindergarten to Grade 8 Social Studies Framework of Outcomes (2003). The purpose is to illustrate the ways in which the social studies curriculum document can uphold, and legitimizes structures of settler colonialism, further marginalizing diverse populations in Manitoba schools. I also explored the ways in which the curriculum uses language to impose settler narratives by privileging Eurocentric perspectives and stories. Furthermore, through this analysis, I examined the ways in which the curriculum neglects to acknowledge ongoing acts of settler colonialism within the larger society of Manitoba. My findings illustrated how the language, narratives and discourses of the curriculum include the “othering” of Indigenous perspectives, upholding prairie settler narratives and futurity, justifying colonization, and promoting a colonial construct of citizenship. The analysis also conveyed the ways in which the omission of specific events, places, people, perspectives, and stories reflect the ways in which Canada is a colonizing entity. Suggestions for curricular revisions include a more inclusive approach to recruiting curriculum writers and to authentically include Indigenous perspectives and epistemologies within the text. It is also recommended that educators and educational leaders invest in opportunities to read and examine other curriculum documents through a critical lens and for professional development to focus specifically on truth and reconciliation.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.027 | 0.023 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.002 | 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".