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Record W4312826400 · doi:10.7202/1091298ar

La voix des jeunes atteints de maladies rares ou peu fréquentes : un récit croisé de leur parcours scolaire

2022· article· fr· W4312826400 on OpenAlexvenueno aff
Auxiliadora Sales Ciges

Bibliographic record

VenueEnfance en difficulté · 2022
Typearticle
Languagefr
FieldSocial Sciences
TopicInclusion and Disability in Education and Sport
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesArt

Abstract

fetched live from OpenAlex

Ce document fait partie d’une étude plus large sur les maladies rares et peu fréquentes et l’inclusion scolaire. La réponse éducative aux élèves souffrant de maladies rares, également appelées maladies orphelines, minoritaires ou à faible prévalence, implique une approche globale des domaines éducatif, sanitaire et psychosocial, ce qui constitue un défi pour les processus d’inclusion mis en oeuvre dans de nombreuses écoles. L’objectif de cette recherche est d’analyser quels processus éducatifs favorisent et entravent l’inclusion des élèves atteints de maladies rares dans les classes ordinaires. La méthodologie du récit biographique nous permet d’analyser en profondeur les expériences des processus d’inclusion à travers les voix de deux jeunes atteints de maladies peu fréquentes qui nous racontent leur parcours scolaire.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0040.002
Scholarly communication0.0040.003
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.018
GPT teacher head0.335
Teacher spread0.317 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

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Citations1
Published2022
Admission routes1
Has abstractyes

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