The Nature of Our Literature
Bibliographic record
Abstract
Scientists rely upon an accurate scientific literature in order to build and test new theories about the natural world. In the past decade, observational studies of the scientific literature have indicated that numerous questionable research practices and poor reporting practices may be hindering scientific progress. In particular, 3 recent studies have indicated an implausibly high rate of studies with positive (i.e., hypothesis confirming) results. In sports medicine, a field closely related to kinesiology, studies that tested a hypothesis indicated support for their primary hypothesis ~70% of the time. However, a study of journals that cover the entire field of kinesiology has yet to be completed, and the quality of other reporting practices, such as clinical trial registration, has not been evaluated. In this study we retrospectively evaluated 300 original research articles from the flagship journals of North America (Medicine and Science in Sports and Exercise), Europe (European Journal of Sport Science), and Australia (Journal of Science and Medicine in Sport). The hypothesis testing rate (~64%) and positive result rate (~81%) were much lower than what has been reported in other fields (e.g., psychology), and there was only weak evidence for our hypothesis that the positive result rate exceeded 80%. However, the positive result rate is still considered unreasonably high. Additionally, most studies did not report trial registration, and rarely included accessible data indicating rather poor reporting practices. The majority of studies relied upon significance testing (~92%), but it was more concerning that a majority of studies (~82%) without a stated hypothesis still relied upon significance testing. Overall, the positive result rate in kinesiology is unacceptably high, despite being lower than other fields such as psychology, and most published manuscripts demonstrated subpar reporting practices
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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 | 0.041 | 0.277 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.023 | 0.017 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.020 | 0.017 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.056 | 0.018 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".