Calibrating the Scientific Ecosystem Through Meta-Research
Open accessPublished version of a preprint
Abstract
While some scientists study insects, molecules, brains, or clouds, other scientists study science itself. Meta-research, or research-on-research, is a burgeoning discipline that investigates efficiency, quality, and bias in the scientific ecosystem, topics that have become especially relevant amid widespread concerns about the credibility of the scientific literature. Meta-research may help calibrate the scientific ecosystem toward higher standards by providing empirical evidence that informs the iterative generation and refinement of reform initiatives. We introduce a translational framework that involves ( a) identifying problems, ( b) investigating problems, ( c) developing solutions, and ( d) evaluating solutions. In each of these areas, we review key meta-research endeavors and discuss several examples of prior and ongoing work. The scientific ecosystem is perpetually evolving; the discipline of meta-research presents an opportunity to use empirical evidence to guide its development and maximize its potential.
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References 225 · Cited by 98
- Authors
- Hardwicke, T. E., Serghiou, S., Janiaud, P., Danchev, V., Crüwell, S., Goodman, S. N., & Ioannidis, J. P. A.
- Affiliations
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin (DE)
- Stanford University (US)
- Corresponding author
- Hardwicke, Tom E.
- ORCID
- Venue
- Annual Review of Statistics and Its Application
- Publisher
- Annual Reviews
- Published
- 2020-03-09
- Volume
- 7
- Issue
- 1
- Pages
- 11-37
- ISSN
- 2326-8298 (ISSN-L)
- 2326-831X
- OpenAlex
- W2926615310
- Cited by
- 98
- References
- 225
- Citations by year
- 2026: 10 · 2025: 11 · 2024: 14 · 2023: 12 · 2022: 20 · 2021: 18 · 2020: 12 · 2019: 1
- FWCI
- 2.21
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Annual Review of Statistics and Its Application (publishedVersion, cc-by)
- Open Access at Essex (University of Essex) (submittedVersion)
- MetArXiv (OSF Preprints) (submittedVersion, cc-by)
- OSF Preprints (OSF Preprints) (submittedVersion)
- MetArXiv (OSF Preprints) (submittedVersion)
- OSF Preprints (OSF Preprints) (submittedVersion, cc-by)
- Funders
- Laura and John Arnold Foundation
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Academic Publishing and Open Access
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › scientometrics and bibliometrics research
- Keywords
- meta-research, research ecosystem, research quality, research bias, research efficiency, research reform
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