Stories and counter-stories from French second language researchers
Bibliographic record
Abstract
Cet article est la continuation d’un chapitre au sujet de l’effacement de la race dans le domaine de l’éducation et de la recherche du français langue seconde (FLS). Nous employons le counter-story et les récits critiques afin de répondre à la question : quelles sont nos expériences qui concernent la race en tant qu’enseignante, chercheuse ou étudiante du FLS? Nous explorons nos expériences racisées en lien avec le concept de alter lingua (l’autre linguistique). Nos histoires démontrent l’effacement des expériences des apprenant.e.s, enseignant.e.s et participant.e.s de recherche racisé.e.s et nous offrons une réflexion sur les hiérarchies raciales et les tensions dans le domaine. Nous visons à rompre le récit maitre (master narrative) déracialisé du FLS et nous invitons nos collègues à y réfléchir. Nous préconisons que les chercheur.e.s du FLS prennent en compte la race au lieu de l’ignorer ou de l’effacer.
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 | 0.015 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.022 | 0.020 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".