Bibliographic record
Abstract
A large amount of variation exists in beliefs about the purpose and benefits of preregistration, making it difficult to implement and evaluate, and limiting its usefulness. Additionally, no single resource exists to describe what a preregistration should contain or how it should be used. In this paper, I describe what an effective preregistration should contain and when it should be used. Specifically, preregistration should 1) restrict as many researcher degrees of freedom as possible, 2) detail all aspects of a study’s method and analysis, 3) detail information on decisions made during the planning stages, and 4) specify how the results will be used and interpreted. Further, a preregistration must be publicly verifiable and permanent. Finally, I argue that preregistration should be used in any situation where researchers intend to collect data in order to make a claim, description, decision, or inference based on that data. I also note that preregistrations which do not address each of these points do more harm than good by falsely signalling credibility and quality.
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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 | 0.263 | 0.494 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.012 | 0.023 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.016 | 0.013 |
| Insufficient payload (model declined to judge) | 0.010 | 0.008 |
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".