Detecting Changes in Simulated Events Using Partial‐Interval Recording and Momentary Time Sampling III: Evaluating Sensitivity as a Function of Session Length
Bibliographic record
Abstract
In a series of two studies, we graphed simulated data representing continuous duration recording and continuous frequency recording into ABAB reversal designs depicting small, moderate, and large behavior changes during 10‐min, 30‐min, and 60‐min sessions. Data sets were re‐scored using partial‐interval recording and momentary time sampling with interval sizes set at 10 s, 20 s, 30 s, 1 min, and 2 min. In study 1, we visually inspected converted data for experimental control and compared the conclusion with those from the respective continuous duration recording or continuous frequency recording data to test for false negatives. In study 2, we evaluated the extent to which interval methods that were sensitive to changes in study 1 produced false positives. In part, the results show that momentary time sampling with interval sizes up to 30 s detected a wide range of changes in duration events and frequency events during lengthier observation periods. The practical implications of the findings are briefly discussed. Copyright © 2011 John Wiley & Sons, Ltd.
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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 | 0.013 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".