Leveraging the Open Science Framework in Clinical Psychological Assessment Research
Bibliographic record
Abstract
The last decade has seen enormous advances in research transparency in psychology. One of these advances has been the creation of a common interface for openness across the sciences – the Open Science Framework (OSF). While social, personality, and cognitive psychologists have been at the fore in participating in open practices on the OSF, clinical psychology has trailed behind. In this paper, we discuss the advantages and special considerations for clinical assessment researchers’ participation in open science broadly, and specifically in using the OSF for these purposes. We use several studies from our lab to illustrate the uses of the OSF for psychological studies, as well as the process of implementing this tool in assessment research. Among these studies are an archival assessment study, a project using an extensive unpublished assessment battery, and one in which we developed a short-form assessment instrument.
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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 | 0.513 | 0.459 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.014 | 0.008 |
| Science and technology studies | 0.011 | 0.080 |
| Scholarly communication | 0.025 | 0.033 |
| Open science | 0.005 | 0.042 |
| Research integrity | 0.007 | 0.018 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".