Children's thoughts on the social exclusion of peers with intellectual or learning disabilities
Bibliographic record
Abstract
BACKGROUND: Previous research has shown that children with intellectual or learning disabilities are at risk for social exclusion by their peers but little is known of children's views on this topic. In this study, we used concept mapping to investigate elementary school children's thoughts on why they believe their peers with intellectual or learning disabilities are sometimes socially excluded at school. METHOD: Participants were 49 grade five and six children who attended inclusive classrooms. Interviews were digitally recorded and transcribed. We extracted 49 unique statements from the transcribed data, and then invited participants to sort the statements into meaningful categories. RESULTS: Sorted data were entered into matrices, which were summed and analysed with multi-dimensional scaling and cluster analysis. A four-cluster solution provided the best conceptual fit for the data. Clusters reflected themes on (1) the thoughts and actions of other children; (2) differences in learning ability and resource allocation; (3) affect, physical characteristics and schooling; and (4) negative thoughts and behaviours. CONCLUSIONS: The overarching reason for social exclusion focused on differences between children with and without disabilities. This study also provided evidence that children are effective, reliable and competent participants in concept mapping. Educational and research implications are discussed.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".