Preregistration of Research on Research Integrity is still Not Common: Findings from the Hong Kong, Cape Town, and Athens Editions of the World Conference on Research Integrity
Bibliographic record
Abstract
Background: This article reports on prevalence of preregistration of empirical studies presented at three editions of the World Conference on Research Integrity (Hong Kong, 2019; Cape Town, 2022; Athens, 2024) at the time of abstract submission, and the association of preregistration with the characteristics of the study and of the researchers submitting the abstract. Method: During registration and abstract submission, applicants were invited to answer questions on preregistration of their study and their academic background. Information collected varied somewhat across conferences, as our insights developed over time. Because of modest sample sizes, we only present descriptive analyses of the prevalence of preregistration and its association with the study’s research theme, and the applicant’s career stage and academic rank. Results: The prevalence of preregistration among presenters of empirical research did not improve across the three WCRIs, and stagnated on average at a modest 28 percent. The verifiability of claims of preregistration did improve over time, however, and increased from 44 to 88 per cent of the abstracts of empirical studies that claimed to have preregistered. . Reasons given for not preregistering varied highly, but little faith in its usefulness and unfamiliarity were frequently mentioned. Younger researchers tended to preregister more often than others, and researchers with a biomedical background preregistered more often. Conclusions: Preregistration of research integrity studies still is not common, and the trend over time suggests stagnation at a rather low level. Reasons for not preregistering participants were too varied to extract one clear-cut solution. We suggest to simplify the preregistration process and propose that funding agencies, research institutes and scholarly journals should demand preregistration of empirical studies.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | MetaresearchResearch integrity Domain: Reproducibility · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| gpt | MetaresearchResearch integrity Domain: Reproducibility · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.046 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.043 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".