Toward an Automation of Functional Analysis Interpretation: A Proof of Concept
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
The advent of functional analysis (FA) methodology paved the way for improved function-based behavioral interventions and ultimately client outcomes. Behavior analysts primarily rely on visual inspection to interpret FA results. However, the literature suggests interpretations may vary across raters resulting in poor interobserver agreement (IOA). To increase interpretation objectivity and address IOA issues, Hagopian et al. created visual-inspection criteria. They reported improved IOA, alongside criteria limitations. Following this, Roane et al. modified these criteria. The current project describes the first steps toward developing a decision support system to assist in FA interpretation. Specifically, we created a computer script, written in R, designed to evaluate FA data and produce an outcome (assign function) based on the Roane et al. criteria. Average agreement between experienced human raters and the computer script outcomes was 81%. We discuss criteria limitations (e.g., vague rules), study implications, and the significance of further research on this topic.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it