Regards pluriels sur les interventions sociales et de santé en contexte de diversité
Bibliographic record
Abstract
Ce numero thematique met en lumiere les travaux realises par les membres de l’equipe de recherche METISS(Migration et Ethnicite dans les Interventions en Sante et Service social). Au cours des quinze dernieres annees,l’equipe METISS est devenue une reference pour ses expertises relatives aux interventions sociales et de sante encontexte de diversite, notamment dans le contexte quebecois. Les chercheurs de l’equipe METISS ont commeobjectif de mieux comprendre les parcours migratoires et les differentes facettes de l’integration des immigrants aleur societe d’accueil. Le lien aux services sociaux et de sante est central a ces interrogations, mais les recherchesentreprises s’interessent aussi a d’autres dimensions pouvant contribuer a l’amelioration des conditions de vie despopulations migrantes, comme le travail, l’interpretariat, l’insertion dans des reseaux de sociabilite ou la prise encompte des savoirs familiaux dans la relation d’aide. De facon generale, ces travaux nous invitent, d’une part, aposer un regard critique sur les conditions qui nuisent a la sante et aux conditions de vie des personnes migranteset, d’autre part, a reflechir a des pratiques d’intervention sociale et de sante plus inclusives. Cette facond’apprehender les enjeux repose sur une conception dynamique de l’ethnicite et de l’immigration qui ne reduit pasle vecu des populations migrantes a des explications culturalistes, mais le situe plutot dans la dynamique desparcours de vie des migrants et des rapports sociaux existant entre la societe d’accueil et les nouveaux arrivants.
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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 | 0.059 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.008 | 0.022 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.012 | 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".