Children's judgements and emotions about social exclusion based on weight
Bibliographic record
Abstract
This study examined children's judgements and emotions associated with weight-based social exclusion using an ethnically diverse sample of one hundred and seventeen 9- and 13-year-old children. Children were interviewed about three scenarios depicting weight-based exclusion in athletic, academic, and social contexts. Children's judgements of exclusion, emotions attributed to the excluder and excluded targets, and justifications for judgements and emotions were examined. Overall, children judged weight-based exclusion to be wrong for moral reasons. However, they viewed weight-based exclusion in athletic contexts as less wrong compared with academic contexts, and they used more social-conventional reasoning to justify judgements and emotions attributed to excluders in athletic contexts compared with academic and social contexts. Children also expected excluded targets to feel negative emotions, whereas a range of positive and negative emotions was attributed to excluders. In addition, older children were more accepting of weight-based exclusion in athletic contexts than in academic and social contexts. We discuss the results in relation to the development of children's understanding of, and emotions associated with, exclusion based on weight.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".