Exploring the characteristics, global distribution and reasons for retraction of published articles involving human research participants: a literature survey
Bibliographic record
Abstract
AIM: Article retraction is a measure taken by journals or authors where there is evidence of research misconduct or error, redundancy, plagiarism or unethical research. Recently, the retraction of scientific publications has been on the rise. In this survey, we aimed to describe the characteristics and distribution of retracted articles and the reasons for retractions. METHODS: We searched retracted articles on the PubMed database and Retraction Watch website from 1980 to February 2016. The primary outcomes were the characteristics and distribution of retracted articles and the reasons for retractions. The secondary outcomes included how article retractions were handled by journals and how to improve the journal practices toward article retractions. RESULTS: We included 1,339 retracted articles. Most retracted articles had six authors or fewer. Article retraction was most common in the USA (26%), Japan (11%) and Germany (10%). The main reasons for article retraction were misconduct (51%, n = 685) and error (14%, n = 193). There were 66% (n = 889) of retracted articles having male senior or corresponding authors. Of the articles retracted after August 2010, 63% (n = 567) retractions were reported on Retraction Watch. Large discrepancies were observed in the ways that different journals handled article retractions. For instance, articles were completely withdrawn from some journals, while in others, articles were still available with no indication of retraction. Likewise, some retraction notices included a detailed account of the events that led to article retraction, while others only consisted of a statement indicating the article retraction. CONCLUSION: The characteristics, geographic distribution and reasons for retraction of published articles involving human research participants were examined in this survey. More efforts are needed to improve the consistency and transparency of journal practices toward article retractions.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | MetaresearchResearch integrity Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | MetaresearchBibliometricsResearch integrity Domain: Evaluation · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.079 | 0.306 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.035 | 0.030 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.011 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".