Curtailing the Use of <i>Preregistration</i> : A Misused Term
Bibliographic record
Abstract
Improving the usability of psychological research has been encouraged through practices such as prospectively registering research plans. Registering research aligns with the open-science movement, as the registration of research protocols in publicly accessible domains can result in reduced research waste and increased study transparency. In medicine and psychology, two different terms, registration and preregistration, have been used to refer to study registration, but applying inconsistent terminology to represent one concept can complicate both educational outreach and epidemiological investigation. Consistently using one term across disciplines to refer to the concept of study registration may improve the understanding and uptake of this practice, thereby supporting the movement toward improving the reliability and reproducibility of research through study registration. We recommend encouraging use of the original term, registration, given its widespread and long-standing use, including in national registries.
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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 | 0.701 | 0.868 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.015 | 0.029 |
| Science and technology studies | 0.005 | 0.039 |
| Scholarly communication | 0.018 | 0.028 |
| Open science | 0.011 | 0.016 |
| Research integrity | 0.014 | 0.025 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".