Children's trust in the testimony of physically disabled or obese individuals
Bibliographic record
Abstract
Previous research has examined various factors that influence children’s trust in testimony. However, no studies have yet looked at children’s willingness to trust physically disabled or obese individuals. Evidence shows that children’s perception of the physically disabled may be both positive and negative, whereas their perception of overweight individuals is negative. Given these attitudes, Study 1 examined the possibility that children may place less trust in these individuals and their testimony. Four- and 5-year-old children were asked to endorse the testimony of one speaker (physically abled and non-obese vs physically disabled/obese) when conflicting testimony was provided. The results showed that children favoured the testimony of the physically abled and non-obese individual at a level significantly above chance. In Study 2, physical condition was pitted against past reliability, and 4- and 5-year-olds were asked to choose between a previously unreliable physically abled and non-obese individual or a previously reliable physically disabled or obese individual. The results indicated that overall children did not show a significant preference for one individual over another. In line with previous findings on children’s negative perceptions of physically disabled and obese individuals, children place less trust in their testimony, and past reliability might cancel out this effect.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".