Monologues from the margins: Voices and experiences of racially minoritized French immersion students
Bibliographic record
Abstract
This doctoral study investigated race and racism in French immersion (FI) programs in Ontario and Canada. Much research in the field of FI prioritizes efficacy of language learning and the studies that do consider equity speak to English language learners, immigrants, socioeconomic status, and special education needs. Meanwhile, racial identity is frequently overlooked entirely or simply not researched, despite long-standing evidence FI is dominated by White people. Despite the racial diversity of the French-speaking world, race has only recently started to be considered in FI. This study investigates racially minoritized students’ experiences in FI to find ways to improve their experiences.\n\nThis exploratory sequential mixed method study aimed to better understand how racially minoritized students experience FI. Stage 1 engaged racially minoritized FI student participants (n=3) in modified Playbuilding (Norris, 2016) to create autoethnographic counter-stories and monologues about their experiences in FI. Participants in Stage 1 also created a list of suggestions and needs to improve FI. Stage 2 of the study employed a qualitative and quantitative online questionnaire. FI Stakeholders across Ontario and Canada (n=39) read and watched the stories and monologues, then reacted to them via the online questionnaire. Stakeholders rated and expanded on the suggestions from Stage 1. Stage 3 included one follow-up interview with a participant from Stage 1.\n\nThematic analysis, critical discourse analysis, content analysis, and descriptive statistics revealed three major findings: 1) there was very little cultural learning—let alone racially and culturally diverse learning— in FI; 2) participants experienced racism in FI and FI teachers and administrators needed to address antiracism; 3) participants in Stage 1 were more concerned about improving their French proficiency than addressing racism. Disturbingly, some participants accepted racism as a foregone conclusion, but could not accept that their French had not improved in years. Findings reveal that there is a pressing need for diverse, culturally responsive and sustaining intercultural teaching in FI as well as training on anticolonial and antiracist pedagogy for teachers. Given the small participant size, this study serves as a launching point for future research into diverse intercultural teaching and learning, and antiracism in FI.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.027 | 0.013 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".