The Effects of Model Prompts on Joint Attention Initiations in Children with Autism
Bibliographic record
Abstract
The general purpose of the current study was to evaluate the effects of minimally intrusive prompting procedures and preferred stimuli on protodeclarative joint attention initiations in children diagnosed with autism spectrum disorder (ASD). Two boys and one girl diagnosed with ASD participated. The experimenter provided attention and social interaction following protodeclarative initiations throughout all phases of the study. During intervention, a model prompt was delivered every 30 s if the participant failed to initiate a bid for joint attention. Results for the first participant show that a model prompt was sufficient to increase the rate of protodeclarative initiations across stimulus sets. Generalization was seen across sets, but not across environments. Subsequently, the model prompt was sufficient to increase the rate of protodeclarative initiations across sets in a second setting (classroom). Results for the second participant are inconclusive. Data collected during the initial baseline condition show that she engaged in an incompatible verbal response across sets. When pictorial stimuli depicting highinterest items and activities were introduced, the rate of protodeclarative initiations increased over time. We then returned to original baseline condition and saw an initial decrease, followed by a steady increase in the rate of protodeclarative initiations. The third participant withdrew prematurely due to medical reasons. The findings of the current study show that minimally intrusive prompts and natural consequences may be sufficient to establish protodeclarative initiations in children. However, this finding may be limited to only those children for whom social interactions already function as reinforcers.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".