<b>Becoming-FSL-Teacher: A Sociomaterial Exploration of Language Teacher Identity in Core French</b>
Bibliographic record
Abstract
This article draws upon a 10-month study of core French as a second language (FSL) teachers to explore the professional identities that are produced in their work. Adopting rhizoanalysis and the Deleuzian concept of becoming, this article positions the ways in which these teachers both affect and are affected by the multiple material and discursive practices that circulate within their local context and Canadian FSL education. Engaging data from classroom observations, interviews, and lesson artifacts, several vignettes are offered as empirical examples that extend insights into how the status of FSL, the material components of the school, and the teachers’ identities are co-constituted. These data entry points are possible lines to think differently about the intricacies of becoming a core FSL teacher in day-to-day practice and the ways in which these educators are constantly (re)shaped in relation with the material, human, and affective elements in their classrooms. These affordances can disrupt existing understandings and suggest how (re)imagining FSL teachers from a sociomaterial perspective might further nuance discussions of retention, support, and identity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.015 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".