Exploring the statistical fragility of creativity studies using the alternate uses task: A systematic review protocol
Bibliographic record
Abstract
INTRODUCTION: The Alternate Uses Task (AUT), where participants generate multiple novel uses for everyday objects, is one of the most widely used measures of creative potential. While many creativity studies report statistically significant effects on AUT outcomes, the robustness of these findings has not been systematically evaluated. Given concerns about over-reliance on p-values and the risk of fragile results, this systematic review aims to assess the statistical fragility of AUT-based findings using the continuous fragility index (CFI). METHODS: This protocol has been registered in Open Science Framework (DOI: 10.17605/OSF.IO/KFQGU). Eligible studies will include two-arm experimental designs (randomized or non-randomized) that report statistically significant between-group differences on AUT outcomes. We plan to conduct a comprehensive search of databases, including Medline, Embase, PsycInfo, and ERIC, up to July 21, 2025. Two reviewers will independently screen studies and perform data extraction. The primary outcome will be the median CFI across included studies, calculated using a simulation-based method developed for continuous outcomes. As a secondary objective, we plan to conduct a multivariable linear regression and subgroup analyses to explore study-level predictors of fragility. DISSEMINATION: We will disseminate results through publication in a peer-reviewed journal and presentations at academic conferences. The findings aim to raise awareness of statistical fragility in creativity research and encourage stronger reporting and methodological practices for studies using the AUT.
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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 | 0.186 | 0.235 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.015 | 0.013 |
| Bibliometrics | 0.020 | 0.014 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.054 | 0.010 |
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".