Teaching French as a Second Language in Canada: Convergence Points of Language, Professional Knowledge, and Mentorship from Teacher Preparation through the Beginning Years
Bibliographic record
Abstract
French as a second language (FSL) teachers in Canada face unique circumstances and challenges in the profession, from their initial teacher preparation into the beginning years of teaching and beyond. These challenges play a role in the long-standing FSL teacher shortage across Canada. To better understand the complexity and nuance of issues facing teachers of FSL in minority settings, we conducted a study in 2021 across different regions in Canada that included 29 focus groups with a total of 89 participants from three key stakeholder groups: teacher educators working in faculties of education; school district and board representatives; and FSL teachers, with a focus on recently graduated novice teachers. In our analysis, we found that participants’ unique and contextualized experiences are framed around two key points of convergence in our data: access and conceptualizations. We present and discuss these findings, considering practical and ideological elements stemming from these points of convergence. We then conclude the paper with a synthesis of the complexities and interconnectedness inherent in the factors related to FSL teacher preparation and support, including a reflection on what this might ultimately tell us about the FSL teacher shortage.
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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.016 | 0.006 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 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".