Bibliographic record
Abstract
Au Canada, un manque criant d’enseignants et d’enseignantes est observé dans les programmes en immersion française (IF) dans toutes les provinces et territoires causé, entre autres, par une augmentation du nombre d’élèves inscrits au rythme de près de 4 % par année depuis plus de 20 ans. Afin de mieux comprendre l’ampleur du phénomène, l’Association canadienne des professionnels de l’immersion (ACPI) a, en 2018, mené une grande Consultation pancanadienne puis, en 2021, a dirigé une enquête visant à chiffrer cette pénurie. Les données sont saisissantes, mais les conséquences de cette pénurie du personnel enseignant en IF sont carrément alarmantes allant d’une réduction de l’offre par manque d’enseignants et d’enseignantes maîtrisant le français à l’assouplissement des critères d’embauche qui pourrait mener à la fragilisation de la qualité du programme.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".