First Impressions Matter: Exploring Children’s Negative Perceptions of Autistic Children
Bibliographic record
Abstract
Many autistic individuals face social challenges that may be due to the negative perceptions of their non-autistic peers. This study investigated school-aged children’s first impressions of autistic and non-autistic children. Thirty-seven children (ages 5-12 years) watched brief videos of autistic and non-autistic children discussing their interests and rated these children’s traits, and their behavioral intentions towards the children. Autistic children were rated as more awkward, aggressive, and less likeable, though the raters’ willingness to interact with the children in the videos was similar for both autistic and non-autistic children. The raters’ negative perceptions of the autistic children were not related to the raters' age, IQ, sex, autistic traits, or social competence. Future work should aim to further investigate what factors influence biases. These findings highlight the need for interventions in school settings to address early perceptions of autism. Educating children about autism can help challenge stereotypes and promote inclusion, ultimately fostering more positive interactions between autistic and non-autistic children.
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 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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".