The SSSPIN study—spin in studies of spin: meta-research analysis
Bibliographic record
Abstract
OBJECTIVES: To identify and calculate the prevalence of spin in studies of spin. DESIGN: Meta-research analysis (research on research). SETTING: 35 studies of spin in the scientific literature. MAIN OUTCOME MEASURES: Spin, categorised as: reporting practices that distort the presentation and interpretation of results, creating misleading conclusions; discordance between results and their interpretation, with presentation of favourable conclusions that are not supported by the data or results; attribution of causality when study design does not support it; and over-interpretation or inappropriate extrapolation of results. RESULTS: Five (14%) of 35 spin studies contained spin categorised as reporting practices that distort the presentation and interpretation of results (n=2) or categorised as over-interpretation or inappropriate extrapolation of results (n=3). CONCLUSION: Spin occurs in research on spin. Although researchers on this topic should be sensitive to spinning their findings, our study does not undermine the need for rigorous interventions to reduce spin across various research fields. CONCLUSION WITH SPIN: Our hypothesis that spin will be less prevalent in spin studies than in studies on other topics has been proven. Spin scholars are less likely to spin their conclusions than other researchers, and they should receive substantial resources to launch and test interventions to reduce spin and research waste in reporting.
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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 | 0.205 | 0.423 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.014 | 0.048 |
| Bibliometrics | 0.018 | 0.013 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".