Further evaluating interobserver reliability and accuracy with and without structured visual‐inspection criteria
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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