Les manifestations de discrimination dans l’évaluation informelle des compétences professionnelles de futur[e]s enseignant[e]s du primaire issu[e]s de la migration en contexte de stage
Bibliographic record
Abstract
Future primary school teachers with a migrant background (FEIMs) represent a population that has been relatively under-researched. Focusing on students from the University of Teacher Education in the canton of Vaud, Switzerland, we have chosen to shed light on certain less-visible aspects of the assessment of their competencies during internships. In particular, we aim to highlight the gap that is sometimes observed between the prescribed evaluation process, including attempts to grant it some degree of objectivity, and the reality of the judgements made about their competencies, judgements apparently influenced by these students’ ties to migration. The findings, from qualitative research based on semi-structured interviews, suggest that, at least in the context of formative and informal evaluations of FEIMs during internships, they are subject to stereotypes and expressions of doubt regarding their competencies, similar to those they have already encountered in their prior educational experiences. Therefore, their teacher training does not seem to shield them from the expression of doubts related to their migrant background.
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 | 0.043 | 0.080 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".