Discrimination Skills Predict Effective Preference Assessment Methods for Adults with Developmental Disabilities
Bibliographic record
Abstract
We examined the relationship between three discrimination skills (visual, visual matching-to-sample, and auditory-visual) and four stimulus modalities (object, picture, spoken, and video) in assessing preferences of leisure activities for 7 adults with developmental disabilities. Three discrimination skills were measured using the Assessment of Basic Learning Abilities Test. Three participants mastered a visual discrimination task, but not visual matching-to-sample and auditory-visual discriminations; two participants mastered visual and visual matching-to-sample discriminations, but not auditory-visual discrimination, and two participants showed all three discriminations. The most and least preferred activities, identified through paired-stimulus preference assessment using objects, were presented to each participant in each of the four modalities using a reversal design. The results showed that (1) participants with visual discrimination alone showed a preference for their preferred activities in the object modality only; (2) those with visual and visual matching-to-sample discriminations, but not auditory-visual discrimination, showed a preference for their preferred activities in the object but not in the spoken modality, and mixed results in the pictorial and video modalities; and (3) those with all three discriminations showed a preference for their preferred activities in all four modalities. These results provide partial replications of previous findings on the relationship between discriminations and object, pictorial, and spoken modalities, and extend previous research to include video stimuli.
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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 | 0.001 | 0.012 |
| 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 | 0.000 |
| 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".