Tools, techniques, methods, and processes for the detection and mitigation of fraudulent or erroneous data in evidence synthesis: a scoping review protocol
Bibliographic record
Abstract
OBJECTIVE: This scoping review aims to identify, catalogue, and characterize previously reported tools, techniques, methods, and processes that have been recommended or used by evidence synthesizers to detect fraudulent or erroneous data and mitigate its impact. INTRODUCTION: Decision-making for policy and practice should always be underpinned by the best available evidence-typically peer-reviewed scientific literature. Evidence synthesis literature should be collated and organized using the appropriate evidence synthesis methodology, best exemplified by the role systematic reviews play in evidence-based health care. However, with the rise of "predatory journals," fraudulent or erroneous data may be invading this literature, which may negatively affect evidence syntheses that use this data. This, in turn, may compromise decision-making processes. INCLUSION CRITERIA: This review will include peer-reviewed articles, commentaries, books, and editorials that describe at least 1 tool, technique, method, or process with the explicit purpose of identifying or mitigating the impact of fraudulent or erroneous data for any evidence synthesis, in any topic area. Manuals, handbooks, and guidance from major organizations, universities, and libraries will also be considered. METHODS: This review will be conducted using the JBI methodology for scoping reviews and reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). Databases and relevant organizational websites will be searched for eligible studies. Title and abstract, and, subsequently, full-text screening will be conducted in duplicate. Data from identified full texts will be extracted using a pre-determined checklist, while the findings will be summarized descriptively and presented in tables. REVIEW REGISTRATION: Open Science Framework https://osf.io/u8yrn.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.333 | 0.327 |
| Meta-epidemiology (narrow) | 0.005 | 0.007 |
| Meta-epidemiology (broad) | 0.008 | 0.014 |
| Bibliometrics | 0.025 | 0.019 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.013 | 0.011 |
| Insufficient payload (model declined to judge) | 0.067 | 0.033 |
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".