La théorie des champs perceptuels et l’interactionnisme symbolique pour comprendre l’intégration scolaire d’élèves présentant une déficience auditive
Bibliographic record
Abstract
Résumé : Bien que dans le discours commun son utilisation soit large, il est souvent difficile de préciser spécifiquement ce à quoi réfère le concept de perception dans les publications scientifiques. Sur près de soixante études recensées sur l’intégration scolaire des élèves présentant une déficience auditive, seules quelques-unes explicitent le concept. Pourtant, il est essentiel de comprendre le concept de perception pour pouvoir mieux l’étudier. Par cette contribution, nous voulons présenter le concept de perception dans une perspective interactionniste et une façon possible d’utiliser ce construit pour étudier l’intégration scolaire d’élèves sourds selon les points de vue d’élèves, de leurs parents et de leurs enseignants. Abstract: Although in common speech its use is wide, it is often difficult to specifically define what the concept of perception in scientific publications is. On nearly sixty studies reviewed on school inclusion of deaf students, only a few explicitly explain the concept. However, it is essential to understand perception to better study it. Through our contribution, we want to introduce the concept of perception in an interactionist perspective and a possible way to use this construct to study the school inclusion of deaf students by the views of students, their parents, and teachers.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 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".