Why Do Manuscripts Get Rejected? A Content Analysis of Rejection Reports from the Indian Journal of Psychological Medicine
Bibliographic record
Abstract
Background: A proportion of manuscripts submitted to scientific journals get rejected, for varied reasons. A systematic analysis of the reasons for rejection will be relevant to editors, reviewers, and prospective authors. We aimed to analyze the reasons for rejection of manuscripts submitted to the Indian Journal of Psychological Medicine, the flagship journal of Indian Psychiatric Society South Zonal Branch. Methods: We performed a content analysis of the rejection reports of all the articles submitted to the journal between January 1, 2018, and May 15, 2020. Rejection reports were extracted from the manuscript management website and divided into three types: desk rejections, post-peer-review rejections, and post-editorial-re-review rejections. They were analyzed separately for the rejection reasons, using a predefined coding frame. Results: A total of 898 rejection reports were available for content analysis. Rejection was a common fate for manuscripts across the types of submission; figures ranged from 26.7% for viewpoint articles to 72.1% for review articles. The median time to desk rejection was 3 days, while the median time to post-peer-review rejection and post-editorial-re-review rejection was 42 days and 96 days, respectively. The most common reasons for desk rejection were lack of novelty or being out of the journal’s scope. Inappropriate study designs, poor methodological descriptions, poor quality of writing, and weak study rationale were the most common rejection reasons mentioned by both peer reviewers and editorial re-reviewers. Conclusions: Common reasons for rejection included poor methodology and poorly written manuscripts. Prospective authors should pay adequate attention to conceptualization, design, and presentation of their study, apart from selecting an appropriate journal, to avoid rejection and enhance their manuscript’s chances of publication.
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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 | 0.115 | 0.494 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.020 | 0.015 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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, 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".