Fate of Manuscripts Rejected by a Specialty Psychiatry Journal: A Retrospective Cohort Study
Bibliographic record
Abstract
Background: (IJPM). Methods: A random sampling of manuscripts was drawn from all submissions rejected between January 1, 2018, and December 31, 2019. Using the titles of these papers and the author names, a systematic search of electronic databases was carried out to examine if these manuscripts have been published elsewhere or not. We extracted data on a range of scientific and nonscientific parameters from the journal's manuscript management portal for every rejected manuscript. Multivariable analysis was used to detect factors associated with eventual publication. Results: Out of 302 manuscripts analyzed, 139 (46.0%) were published elsewhere; of these, only 18 articles (13.0%) were published in a journal with higher standing than IJPM. Manuscripts of foreign origin (odds ratio [OR] 1.77, 95% confidence interval [CI] = 1.06-2.97) and rejection following peer review or editorial re-review (OR 2.41, 95% CI = 1.22-4.74) were significantly associated with publication. Conclusion: Nearly half of the papers rejected by IJPM were eventually published in other journals, though such papers are more often published in journals with lower standing. Manuscripts rejected following peer review were more likely to reach full publication status compared to those which were desk rejected.
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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".