Open Science at <i>Clinical Psychological Science</i> : Reflections on Progress, Lessons Learned, and Suggestions for Continued Improvement
Bibliographic record
Abstract
Open science is challenging and frequently time-consuming work, but the payoff is greater assurance that published research is transparent, conducted rigorously, and protected against some forms of researcher bias. In this editorial, we reflect on progress made toward the integration of open-science practices at Clinical Psychological Science ( CPS) 7 years after badges were introduced in the journal and 3 years after open science was initiated as an editorial priority at CPS. Along with establishing open science as an editorial priority, the first team of Open Science Advisors was established to oversee and facilitate preregistration, open materials, and open data badge applications. Here, we discuss how these practices have evolved over time, highlight best practices and common challenges in this work, and emphasize next steps for the future of open science in clinical-psychology research.
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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 | 0.399 | 0.628 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.008 | 0.021 |
| Scholarly communication | 0.039 | 0.041 |
| Open science | 0.008 | 0.011 |
| Research integrity | 0.028 | 0.051 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".