Unraveling retraction dynamics in COVID-19 research: Patterns, reasons, and implications
Bibliographic record
Abstract
Amid the COVID-19 pandemic, while the world sought solutions, few scholars exploited the situation for personal gains through deceptive studies and manipulated data. This paper presents the extent of 400 retracted COVID-19 papers listed by the RetractionWatch database until the month of February 2024. The primary purpose of the research was to analyze journal quality and retractions trends. Evaluating the journal’s quality is vital for stakeholders, as it enables them to effectively address and prevent such incidents and their future repercussions. The present study found that one-fourth of publications were retracted within the first month of their publication, followed by an additional 6% within six months of publication. One third of the retractions originated from Q1 journals, with another significant portion coming from Q2 (29.8%). An analysis of the reasons for retractions indicates that a quarter of retractions were attributed to multiple causes, predominantly associated with publications in Q2 journals, while another quarter were linked to data issues, primarily observed in Q1 publications. Elsevier retracted 31% of papers, with the majority published as Q1, followed by Springer (11.5%), predominantly as Q2. The study also examined author contributions, revealing that 69.3% were male, with females (30.7%) mainly holding middle author positions.
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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 | MetaresearchResearch integrity Domain: Evaluation · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Observational | low |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.132 | 0.000 |
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".