L’harmonie de la musique et l’identité linguistique : l’a/r/tographie avec des futurs enseignants
Bibliographic record
Abstract
En employant l’a/r/tographie, ce texte tissera le lien entre la musique et l’identité linguistique en éducation. À travers mes expériences en tant qu’enseignante du français au niveau secondaire en milieu minoritaire francophone au Canada et comme professeure à la Faculté d’éducation, j’évoque les possibilités de l’a/r/tographie et les risques de cette méthodologie. Exploiter la musique au moyen de l’a/r/tographie peut susciter un intérêt envers la langue française chez les élèves et les étudiants, pour ensuite contribuer à la valorisation de l’identité francophone. Ce parcours identitaire linguistique raconte la diversité des espaces dynamiques de l’enseignement et le potentiel d’échanges positifs dans ces espaces pour l’identité linguistique des futurs enseignants et des professeurs.
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".