Why are new French immersion and French as a second language teachers leaving the profession? Results of a Canada-wide survey
Bibliographic record
Abstract
Although several provinces have complained\nabout the shortage of French\nimmersion and French as a second\nlanguage (FSL) teachers, many are\nalso wondering why so many teachers are\nleaving the profession in the first few years.\nIngersoll (2001), who calls this attrition a “revolving\ndoor,” was one of the first to blame\nthe teacher shortage on the departure of\nnew teachers and not just the retirement of\nveterans. Borman and Dowling (2008) present\na highly interesting historical overview\nof perspectives on this phenomenon, which\nparticularly affects new teachers. What’s\ngoing on? Are they badly prepared? Are the\nstudents too difficult? Has teaching French\nbecome such a demanding and time-consuming\njob that so many are deserting so quickly?\nWhat are the main problems that teachers\nhave to deal with? What could school\nsystems do to help retain teachers? Based\non these research questions, the Canadian\nAssociation of Immersion Teachers (CAIT),\njointly with the Centre de recherche sur la formation\net la profession enseignante (CRIFPE),\nundertook a Canada-wide survey funded\nby the Department of Canadian Heritage to\nexplore an issue that is of vital importance\nto many education ministries in Canada.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".