Monologues from the Minoritized: Racialized Students’ Experiences in French Immersion
Bibliographic record
Abstract
This paper presents three monologues created from an arts-based doctoral study investigating race and racism in French immersion programs in Ontario. French immersion has been criticized for being exclusionary, especially based on race ( Yoon & Gulson, 2010 ), special education status ( Wise, 2011 ), nationality, and home language ( Mady, 2013 ). In this study, three racialized minority French immersion students shared counter-stories and created monologues highlighting their overall thoughts and experiences related to race and racism in French immersion. Two participants created videos to accompany their monologue, while the third created an audio recording. Participant monologues are analyzed through the lens of critical race theory ( Ladson-Billings & Tate, 1995 ), using thematic analysis ( Nowell, Norris, White, & Moules, 2017 ) and critical discourse analysis ( Wodak & Meyer, 2016 ). Findings show that student experiences were negatively impacted by racism and lack of representation in French immersion programs. Racism was rarely challenged by teachers, administrators, or peers. Despite participants being upset about racism, racism was expected and inevitable. Indeed, participants were more concerned about French proficiency than the racism they were experiencing. These findings show a concerning need for anti-racist action and pedagogy in French immersion. Given the small sample size, more research investigating race in French immersion is needed.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.008 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".