Prevalence of Transparent Research Practices in Psychology: A Cross-Sectional Study of Empirical Articles Published in 2022
Why this work is in the frame
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Bibliographic record
Abstract
More than a decade of advocacy and policy reforms have attempted to increase the uptake of transparent research practices in the field of psychology; however, their collective impact is unclear. We estimated the prevalence of transparent research practices in (a) all psychology journals (i.e., field-wide), and (b) prominent psychology journals, by manually examining two random samples of 200 empirical articles ( N = 400) published in 2022. Most articles had an open-access version (field-wide: 74%, 95% confidence interval [CI] = [67%, 79%]; prominent: 71% [64%, 77%]) and included a funding statement (field-wide: 76% [70%, 82%]; prominent: 76% [70%, 82%]) or conflict-of-interest statement (field-wide: 76% [70%, 82%]; prominent: 73% [67%, 79%]). Relatively few articles had a preregistration (field-wide: 7% [2.5%, 12%]; prominent: 14% [8.5%, 19%]), materials (field-wide: 16% [9%, 24%]; prominent: 19% [12%, 27%]), raw/primary data (field-wide: 14% [7%, 21%]; prominent: 16% [9.5%, 24%]), or analysis scripts (field-wide: 8.5% [4.5%, 13%]; prominent: 14% [9.5%, 19%]) that were immediately accessible without contacting authors or third parties. In conjunction with prior research, our results suggest transparency increased moderately from 2017 to 2022. Overall, despite considerable infrastructure improvements, bottom-up advocacy, and top-down policy initiatives, research transparency continues to be widely neglected in psychology.
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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 | 0.271 | 0.246 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.032 | 0.184 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.001 | 0.006 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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