Toward an Automation of Functional Analysis Interpretation: A Proof of Concept
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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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 | 0.019 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".