Structured visual analysis of single‐case experimental design data: Developments and technological advancements
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Visual analysis is the primary method used to interpret single-case experimental design (SCED) data in applied behavior analysis. Research shows that agreement between visual analysts can be suboptimal at times. To address the inconsistent interpretations of SCED data, recent structured visual-analysis technological advancements have been developed. To assess the extent to which structured visual analysis is used to guide or supplement applied behavior analysts' interpretation of SCED graphs, a systematic review between the years of 2015 to 2020 in the Journal of Applied Behavior Analysis was conducted. Findings showed that despite recent efforts to develop structured visual-analysis tools and criteria, these methods are rarely used to analyze SCED data. An overview of structured visual-analysis tools is shared, their utility is delineated, common characteristics are brought to light, and future directions for both research and their clinical use are highlighted.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 it