Machine Learning to Analyze Single-Case Graphs: A Replication and Extension with Nonsimulated Data
Bibliographic record
Abstract
Machine learning algorithms may adequately control for Type I error rate and power when analyzing single-case AB graphs, but the most promising models have mainly been evaluated on simulated data. Moreover, the characteristics of the graphs that contribute to decision errors remain undocumented. To address these issues, we applied two machine learning models to a previously published nonsimulated dataset containing nearly 17,000 AB graphs showing no change to examine the proportion of false positives. On average, one of the two models (i.e., support vector classifier) produced lower proportions of false positives than well-established methods to analyze AB graphs (i.e., the dual-criteria methods). Larger mean differences between the two phases, lower standard deviations, and negative trends all led to more false positives. These results further support the use of machine learning to analyze single-case graphs, but further replications by independent research teams using educational and clinical data remain necessary.
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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 | 0.182 | 0.502 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.008 | 0.003 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".