PsychDisclosure.org
Bibliographic record
Abstract
There is currently an unprecedented level of doubt regarding the reliability of research findings in psychology. Many recommendations have been made to improve the current situation. In this article, we report results from PsychDisclosure.org, a novel open-science initiative that provides a platform for authors of recently published articles to disclose four methodological design specification details that are not required to be disclosed under current reporting standards but that are critical for accurate interpretation and evaluation of reported findings. Grassroots sentiment-as manifested in the positive and appreciative response to our initiative-indicates that psychologists want to see changes made at the systemic level regarding disclosure of such methodological details. Almost 50% of contacted researchers disclosed the requested design specifications for the four methodological categories (excluded subjects, nonreported conditions and measures, and sample size determination). Disclosed information provided by participating authors also revealed several instances of questionable editorial practices, which need to be thoroughly examined and redressed. On the basis of these results, we argue that the time is now for mandatory methods disclosure statements for all psychology journals, which would be an important step forward in improving the reliability of findings in psychology.
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.
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 | 0.012 | 0.131 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.427 | 0.167 |
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".