Including Empirical Prior Information in the Reliable Change Index
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
The reliable change index (RCI; Jacobson & Truax, 1991) is commonly used to assess whether individuals have changed across two measurement occasions, and has seen many augmentations and improvements since its initial conception. In this study, we extend an item response theory version of the RCI presented by Jabrayilov et al. (2016) by including empirical priors in the associated RCI computations whenever group-level differences are quantifiable given post-test response information. Based on a reanalysis and extension of a previous simulation study, we demonstrate that although a small amount of bias is added to the estimates of the latent trait differences when no true change is present, including empirical prior information will generally improve the Type I behavior of the model-based RCI. Consequently, when non-zero changes in the latent trait are present the bias and sampling variability are show to be more favorable than competing estimators, subsequently leading to an increase in power to detect non-zero changes.
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 | 0.014 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it