Predicting the Learning Ability of Children with Autism: The Assessment of Basic Learning Abilities Test versus Parents' Predictions
Bibliographic record
Abstract
The Assessment of Basic Learning Abilities (ABLA) test is a useful assessment and training tool for persons with developmental disabilities. The present study assessed the predictive validity of the ABLA test with 16 children diagnosed with an autistic spectrum disorder, eight who performed at ABLA Level 4 and eight who performed at ABLA Level 6. Twenty criterion tasks were selected, four at each of five ABLA levels. Predictions were made based on ABLA test performance and by parents as to whether each child would learn each of the criterion tasks (given certain conditions). The researchers then attempted to teach the 20 criterion tasks to each child until they reached either the pass standard or the fail standard of the ABLA test. Ninety-four percent of predictions based on ABLA performance were confirmed, and the ABLA test was significantly more accurate for predicting a child's performance than were parents.
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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 | 0.028 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".