Developing an Anti-Biased, Anti-Racist Stance in Second Language Teacher Education Programs
Bibliographic record
Abstract
Addressing race/racism and colonialism in French as a second language (FSL) education is essential to preparing culturally responsive teachers and meeting the Ministry mandate to teach students equitably and with respect. This article describes whether, and if so, how, candidates are being prepared to disrupt colonial ideologies and practices with data from a three-year project on FSL teacher preparation in two Ontario faculties of education. Interviews were conducted with professors and teacher candidates. Using a critical qualitative approach to identify emerging themes, the study applied an anti-biased, anti-racist (ABAR) lens to identify racialized power inequities that can form across French languages, cultures, and marginalized groups, oppressive common-sense principles, and systemic influences around three main themes: teaching culture and promoting intercultural competence; addressing equity, inclusion, and racism explicitly; and Whiteness, Eurocentrism, and representation in FSL. Findings indicate that while programs have begun to integrate equity, inclusion, interculturality, and the representation of the global francophonie, there is a pressing need for more preparation and training for professors and teacher candidates to develop critical equity skills that lead to their becoming culturally responsive teachers. Practical strategies and theory building through collaborative research are needed to support educators in taking up an ABAR stance in FSL teacher education programs.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".