Writing and Teaching Curriculum With Relationships in Our Place: A Critical Meta-analysis of Saskatchewan Core French Curricula's Cultural Indicators
Bibliographic record
Abstract
In 2011, The Core French curriculum for Levels 1 through 5 were renewed; and Levels 6 through 9 were renewed in 2012. In 2015, the Truth and Reconciliation Committee of Canada released 94 Calls to Action to address the legacy residential schools left on Canadian Indigenous peoples, as well as to move Canada forward in reconciliation. In education, there are seven calls to action related to legacy and four calls to action related to reconciliation. Using document analysis methodology and honouring Indigenous methodology as theoretical frameworks, I organized two focus group interviews to examine if the Saskatchewan Core French Level 1-7 cultural indicators in the renewed documents met the calls to action, specifically the principle of developing culturally appropriate curricula. Senator Murray Sinclair said Indigenous children must be able to answer the following questions: Where do I come from; where am I going; why am I here; and who am I? (Sinclair, 2016). I framed my research along Senator Sinclair’s four questions as I studied the cultural outcomes and indicators of the Core French Level 1-7 curricula. In the thesis title, meta-analysis refers to how the cultural indicators were first critiqued by focus group participants and researcher individually, but then also analyzed together as one research study. It is my hope that the Core French curricula can be a document that respects the Truth and Reconciliation’s calls to action for education, specifically that of culturally appropriate curricula.
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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 | 0.074 | 0.112 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.011 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".