On the prevalence and scientific impact of duplicate publications in different scientific fields (1980‐2007)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose The issue of duplicate publications has received a lot of attention in the medical literature, but much less in the information science community. This paper aims to analyze the prevalence and scientific impact of duplicate publications across all fields of research between 1980 and 2007. Design/methodology/approach The approach is a bibliometric analysis of duplicate papers based on their metadata. Duplicate papers are defined as papers published in two different journals having: the exact same title; the same first author; and the same number of cited references. Findings In all fields combined, the prevalence of duplicates is one out of 2,000 papers, but is higher in the natural and medical sciences than in the social sciences and humanities. A very high proportion (>85 percent) of these papers are published the same year or one year apart, which suggest that most duplicate papers were submitted simultaneously. Furthermore, duplicate papers are generally published in journals with impact factors below the average of their field and obtain lower citations. Originality/value The paper provides clear evidence that the prevalence of duplicate papers is low and, more importantly, that the scientific impact of such papers is below average.
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.
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 | 0.017 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.020 | 0.034 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it