Dissocier allophonie et vulnérabilité : de la nécessité d’interroger les catégories
Bibliographic record
Abstract
This contribution questions the creation of categories of students as established by the French-speaking Belgian education authorities (Fdration Wallonie-Bruxelles, FWB) concerning the schooling of newcomers. More precisely, we are asking if the link established by these authorities doesn't create an amalgam between newcomer students and these students called vulnerable; they are supposedly linked together by the fact that they do not master the language of instruction, or French as the language of learning (franais langue d'apprentissage, FLA). While these categories and the links they create between the students are based on a global and laudable objective of helping the schooling of newcomer students, we think that these categories and the links they create reinforce the negative perception people often have of this public and does not sufficiently recognise the diversity of the schooling public as a whole. We therefore argue for a dissociation between "allophones" and vulnerable students.
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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 | 0.017 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.026 |
| Scholarly communication | 0.010 | 0.019 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".