Social Fringe Dwellers: Can chat-bots combat bullies to improve participation for children with autism?
Bibliographic record
Abstract
Autism Spectrum Disorder (ASD) can cause a gulf in communication that casts children with autism to the fringes of social and family life, despite the best efforts of their carers. These children often struggle with social interaction, lack of interest and empathy, and require intensive therapy to improve their ability to communicate with others. Improvements in social interaction are often hampered by experiences in which children with autism are more susceptible to being bullied. Social and communication technologies (e.g. smartphones and tablets), which children with autism tend to gravitate toward, and to which many families have access, may play a significant future role in building resilience and improving social interaction. Based on technology reviews and stakeholder interviews, we are developing modules for a machine learning artificial intelligence platform (a chat-bot) that assists children attending an Australian mainstream school to recognise and respond to social bullying and sarcasm, allowing bullied autistic children to develop the social prowess to withstand their aggressors.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".