It's (in)escapable: Critically reflecting on a second language curriculum in a settler colonial context
Bibliographic record
Abstract
Abstract In this article, we critically examine the Ontario, Canada secondary French as a second language (FSL) curriculum to unpack the ways it both resists and perpetuates colonial, racist, and oppressive discourses. By engaging in this analysis, we aim to inspire language teacher educators to envision and critically engage with alternative, anticolonial, feminist frameworks that challenge dominant paradigms by developing critical reflection, contextual awareness, and fostering equity‐minded language teacher education (LTE). We encourage a shift from merely recognizing inequities to actively reimagining curriculum and pedagogical practices that promote inclusivity, equity, and justice in LTE. To launch our inquiry, we collaboratively examined the curriculum in NVivo to uncover what is said or not said using critical discourse analysis anchored within a feminist anticolonial framework. Findings reveal that, although the curriculum advocates for inclusion and diversity in its introduction, the document superficially includes diversity throughout without white settler accountability and avoids diverging from a liberal multiculturalism framework, allowing white settler privilege to remain intact. Discussion of the findings examines the implications of these illusions of inclusion and the necessity of critically reflecting on oppressive, settler colonial systems and pedagogies in language education and LTE.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.031 | 0.033 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| 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".