The Predictive Validity of the Assessment of Basic Learning Abilities versus Parents’ Predictions with Children with Autism
Bibliographic record
Abstract
The Assessment of Basic Learning Abilities (ABLA) is an empirically validated clinical tool for assessing the learning ability of persons with intellectual disabilities and children with autism. An ABLA tester uses standardized prompting and reinforcement procedures to attempt to teach, individually, each of six tasks, called levels, to a testee, until a pass or a fail criterion is met on each level. The six levels are ordered in difficulty from Level 1 to Level 6. We examined the predictive validity of the ABLA performance of nine children with autism who passed ABLA Levels 2 or 3, and failed higher levels. We attempted to teach 20 criterion tasks to each child, using standardized prompting and reinforcement procedures, until each child met either the pass or the fail criterion of the ABLA on each task. We predicted that each child would pass the criterion tasks that corresponded to his/her previously passed ABLA levels, and would fail the criterion tasks that corresponded to his/her previously failed ABLA levels. A parent of each child was also asked to predict the child's pass-fail learning performance on the 20 criterion tasks. Ninety-two percent of the predictions based on the children's ABLA performance were confirmed, and the ABLA was significantly more accurate than the parents for predicting the children's performance on the criterion tasks.
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 | 0.010 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".