Predictive validity of auditory matching tasks, verbal behavior, and the ABLA test
Bibliographic record
Abstract
The Assessment of Basic Learning Abilities (ABLA) test has proven to be a useful assessment and training tool for staff responsible for training persons with developmental disabilities. The ABLA test assesses a person's ability to learn six tasks that are hierarchically ordered in level of difficulty. Two auditory matching tasks, a prototype visual-auditory nonidentity matching (VANM) task and a prototype auditory-auditory nonidentity matching (AANM) task have been demonstrated to be more difficult than ABLA level 6, and are hierarchically ordered in difficulty in relationship to each other. As a preliminary step to adding the VANM and AANM tasks to the ABLA test, Experiment I assessed the predictive validity of the VANM and AANM prototype tasks, as well as a third task, auditory-auditory identity matching (AAIM). A Chi-square analysis showed that the prototype VANM, AAIM, and AANM tasks demonstrate predictive validity. Experiment 2 examined where echoics, tacts, and mands fit in level of difficulty in relation to the ABLA tasks and the auditory matching tasks. Using order analysis, echoics, tacts and mands were all found to be more difficult than ABLA levels 3, 4, and 6. Pairwise comparisons found echoics to be less difficult than tacts and mands. Limitations of the experiments and areas of future research are discussed.
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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 | 0.013 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".