Machine Learning for Supplementing Behavioral Assessment
Bibliographic record
Abstract
The Questions About Behavioral Function (QABF) has a high degree of convergent validity, but there is still a lack of agreement between the results of the assessment and the results of experimental function analysis. Machine learning (ML) may improve the validity of assessments by using data to build a mathematical model for more accurate predictions. We used published QABF and subsequent functional analyses to train ML models to identify the function of behavior. With ML models, predictions can be made from indirect assessment results based on learning from results of past experimental functional analyses. In Experiment 1, we compared the results of five algorithms to the QABF criteria using a leave-one-out cross-validation approach. All five outperformed the QABF assessment on multilabel accuracy (i.e., percentage of predictions with the presence or absence of each function indicated correctly), but false negatives remained an issue. In Experiment 2, we augmented the data with 1,000 artificial samples to train and test an artificial neural network. The artificial network outperformed other models on all measures of accuracy. The results indicated that ML could be used to inform conditions that should be present in a functional analysis. Therefore, this study represents a proof-of-concept for the application of machine learning to functional assessment.
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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 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".