Retracted articles in scientific literature: A bibliometric analysis from 2003 to 2022 using the Web of Science
Post-publication record
OpenAlex flags this work as retracted, but it carries no matching Retraction Watch record in this frame.
Bibliographic record
Abstract
Retractions serve a crucial role in maintaining the integrity and accuracy of scientific literature. There has been growing interest in understanding the patterns behind retractions. This bibliometric study analyzed retracted articles published between 2003 and 2022, indexed by the Science Citation Index Expanded of the Web of Science Core Collection database. A total of 8466 retracted articles were identified, revealing an overall increase up to 2019, followed by a decline. A total of 109 countries contributed to the retracted articles, with China and the United States having the highest absolute numbers. In addition, the articles were published in 2347 different journals, with Tumor Biology recording the largest number of retracted articles. The top 10 most cited retracted articles indicated that data and image integrity issues were the main reasons for retraction. The primary reasons for retractions, identified by linking the retracted articles to the Retraction Watch Database, were data and results issues followed by plagiarism and duplication. In conclusion, the present bibliometric study offered an overview of the status of retracted articles indexed by the Web of Science Core Collection over the past two decades. These findings provide insight into areas where scientific integrity may be compromised and serve as a guide to foster a responsible research environment.
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
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 | MetaresearchBibliometricsResearch integrity Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | BibliometricsMetaresearchResearch integrity Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.012 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.113 | 0.164 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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.
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".