Further Analysis of the Predictive Effects of a Free-Operant Competing Stimulus Assessment on Stereotypy
Bibliographic record
Abstract
We conducted five experiments to evaluate the predictive validity of a free-operant competing stimulus assessment (FOCSA). In Experiment 1, we showed that each participant's repetitive behavior persisted without social consequences. In Experiment 2, we used the FOCSA to identify high-preference, low-stereotypy (HP-LS) items for 11 participants and high-preference, high-stereotypy (HP-HS) items for nine participants. To validate the results of the FOCSAs (Experiment 3), we used a three-component multiple schedule to evaluate the immediate and subsequent effects of an HP-LS stimulus, an HP-HS stimulus, or both (in separate test sequences) on each participant's stereotypy. Results of Experiment 3 showed that the FOCSA correctly predicted the immediate effect of the HP-LS stimulus for 10 of 11 participants; however, the FOCSA predictions were less accurate for the HP-HS stimulus. Results of Experiment 4 showed that a differential reinforcement of other behavior procedure in which participants earned access to the HP-LS for omitting vocal stereotypy increased all five participants' latency to engaging in stereotypy; however, clinically significant omission durations were only achieved for one participant. Experiment 5 showed that differential reinforcement of alternative behavior in which participants earned access to the HP-LS stimulus contingent upon correct responses during discrete-trial training reduced targeted and nontargeted stereotypy and increased correct academic responding for all four participants. The potential utility of the FOCSA is 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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".