Reconciling language anxiety and the ‘Montréal switch’: An autoethnography of learning French in Montréal and negotiating my Canadian identity through language
Bibliographic record
Abstract
In this autoethnography, I explore and attempt to make sense of the tensions I experienced in negotiating my identity as an adult learner of French as an additional language in Montréal. I draw from critical sociolinguistics (Lamarre, 2013; Heller, 2007) and Norton’s (2013) definition of identity to discuss and analyse my experiences of the world beyond the language classroom. I explore how the ‘Montréal switch’, when speakers of French responded to me in English when I spoke to them in French, was a particular site of struggle. I discuss how notions of citizenship and belonging intersected with my feelings of language anxiety and the Montréal switch, and attempt to unpack my assumptions and (mis)perceptions of these experiences. My analyses bring additional light to Montréal’s complex sociolinguistic dynamic and the lived experiences of language learners.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.052 | 0.036 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.005 | 0.014 |
| Insufficient payload (model declined to judge) | 0.006 | 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".