Capturing Preferences of Non-verbal Autistic Children While Watching Cartoons on YouTube
Bibliographic record
Abstract
Non-verbal autistic children (NVAC) often learn their first language by watching videos on digital devices. One of the peculiarities of autistic language is the phenomenon of “unexpected bilingualism”, where the first words are spoken in a language other than the language of their parents. How they learn language by watching and which content appeals to them is unclear, but autistic children focus on cartoons, especially those with letters, numbers, shapes, animals, and vehicles, which form most of their first words they produce. This paper presents an ongoing study that examines NVAC interactions with the screen while watching YouTube on a tablet and correlates these indirect feedback signals with video content. We outline possible NVAC interaction events— touches, pauses, or skips—and suggest how they can be interpreted as indicators of engagement or disengagement. By matching these children’s interaction events to each cartoon’s visual and textual components, our approach can detect coarse-grained (overall video preference) and fine-grained (specific moments or objects of interest) NVAC preferences. Future work will test these assumptions in longitudinal NVAC studies and create a personalized system that supports NVAC language acquisition and helps psychologists understand how they learn language through watching cartoons.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".