Reasons for Rejection of Manuscripts Submitted to <i>AJR</i> by International Authors
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to promote publication by international authors in AJR by analyzing the reasons for rejection of manuscripts. MATERIALS AND METHODS: Data available through the electronic system for review of submitted manuscripts were analyzed over a 2-year period with regard to country of origin, type of the manuscript, decision of the editors, and reason for rejection. Countries with more than 50 submitted manuscripts were selected, and rejection rates and reasons for rejection determined by one of the editors were compared. RESULTS: Eighteen countries had more than 50 manuscript submissions, and the rejection rates ranged from 22.6% to 73.4%. Countries with high rates of submission of reports of original research, including Clinical Observations manuscripts, had high acceptance rates. Countries in which English is the primary language had higher acceptance rates than those in which English is not the primary language (29.1% vs 40.3%, p < 0.05). Countries with English as the primary language, including Canada, the United Kingdom, and Australia, had rejection patterns similar to that of the United States. Language problems were not a major reason for rejection, except for manuscripts from China. Lack of new or useful knowledge was by far the most common reason for rejection in all countries (44-76% of all rejections). CONCLUSION: High-quality scientific work is key to overcoming barriers to publication. Designing an appropriate study that answers a clearly defined and pertinent question is an important first step. Language problems were not a major cause of rejection, except for manuscripts from China.
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 | 0.198 | 0.433 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".