Want to try a registered report? Here are our lessons learned.
Bibliographic record
Abstract
A Registered Report is a type of research journal article in which the introduction, methods, and analysis plan are proposed and peer-reviewed prior to the execution of the study. The goal is to limit publication bias based on study findings by conducting peer review on the merits of the study before the results are known. First introduced in 2012 (Chambers, 2013; Chambers & Tzavella, 2022), this format of journal article publication has become more commonplace. Here we provide an overview of the format as well as eight core lessons we learned while preparing Registered Reports. We integrate guidelines from the literature with our experience to provide insight into the process of preparing and publishing a Registered Report for those who have not yet tried it. Though Registered Reports require researchers to invest more effort at the earlier stages of idea generation, design, and analysis planning, they will benefit from the feedback of reviewers when it is most beneficial and leave behind the fear of rejection due to unanticipated study limitations or null results. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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 | 0.460 | 0.798 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.034 | 0.056 |
| Open science | 0.010 | 0.010 |
| Research integrity | 0.019 | 0.029 |
| Insufficient payload (model declined to judge) | 0.018 | 0.014 |
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".