Could machine learning fuel a reproducibility crisis in science?
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Fake or unreproducible research threatens research credibility and public trust.
“Universities must address serious risks including fake or unreproducible research that threaten credibility (Gibney, 2022;Liverpool, 2023) and public trust.”
From A university framework for the responsible use of generative AI in research (2026) · cites this paper as Gibney, 2022
Cites
Twitter mood predicts the stock market
Bollen, J., Mao, H., & Zeng, X. (2011) · Journal of Computational Science · doi:10.1016/j.jocs.2010.12.007 · Cited by 5,206 · see citing works
Muchlinski, D., Siroky, D., He, J., & Kocher, M. (2016) · Political Analysis · doi:10.1093/pan/mpv024 · Cited by 234 · see citing works
Flagged first, then by citations
Cited by
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Sokol, K., Fackler, J., & Vogt, J. E. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01725-9 · Cited by 105 · see citing works
A university framework for the responsible use of generative AI in research
Smith, S. M., Tate, M., Freeman, K., Walsh, A., Ballsun-Stanton, B., & Lane, M. (2026) · Journal of Higher Education Policy and Management · doi:10.1080/1360080x.2025.2509187 · Cited by 46 · see citing works
Berisha, V., & Liss, J. M. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01199-1 · Cited by 41 · see citing works
- Authors
- Gibney, E.
- Corresponding author
- Gibney, Elizabeth
- Venue
- Nature
- Published
- 2022-07-26
- Volume
- 608
- Issue
- 7922
- Pages
- 250-251
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W4288034514
- Cited by
- 74
- References
- 2
- Citations by year
- 2026: 17 · 2025: 13 · 2024: 17 · 2023: 23 · 2022: 3
- Type
- other
- Language
- en
- Open access
- closed
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Scientific Computing and Data Management
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Keywords
- data leakage, replication crisis, machine learning reliability, research reproducibility
- MeSH terms
- Machine Learning, Research, Science, Reproducibility of Results
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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