Further evaluating interobserver reliability and accuracy with and without structured visual‐inspection criteria
Bibliographic record
Abstract
Abstract Visual inspection is the primary method of interpreting functional analysis (FA) outcomes, even though it has occasionally been criticized for producing low levels of interobserver agreement. Researchers have addressed this issue by creating structured visual‐inspection criteria to guide visual inspection of FA outcomes (e.g., Hagopian et al., 1997, https://doi.org/10.1901/jaba.1997.30‐313 ; Roane et al., 2013, https://doi.org/10.1002/jaba.13 ). The purpose of the current study was to systematically replicate and extend Study 1 of Roane et al. (2013, https://doi.org/10.1002/jaba.13 ). We did this by evaluating the reliability and accuracy of 15 novice participants’ visual inspection of 84 FA graphs with and without the modified visual‐inspection criteria developed by Roane et al. Accuracy was markedly higher when participants used the modified visual‐inspection criteria relative to when they used traditional visual‐inspection strategies, while we observed more modest increases in reliability coefficients. Results are discussed in the context of practical and clinical implications of the modified visual‐inspection criteria and suggestions for future research.
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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 | 0.171 | 0.417 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".