Nonscientific Factors Associated with Acceptance for Publication in The Journal of Bone and Joint Surgery (American Volume)
Bibliographic record
Abstract
BACKGROUND: While it is widely accepted that scientific factors may render a study more likely to be accepted for publication, it is less clear whether nonscientific factors may also be associated with publication. The purpose of this study was to identify the nonscientific factors associated with acceptance for publication by The Journal of Bone and Joint Surgery (American Volume). METHODS: A total of 1173 manuscripts submitted to The Journal of Bone and Joint Surgery between January 1, 2004, and June 30, 2005, for publication as scientific articles were analyzed as part of a study on publication bias in the editorial decision-making process. Information was collected on nonscientific factors plausibly associated with acceptance for publication, including study location, conflict-of-interest disclosure, sex of the author, primary language, and the number of prior publications by the corresponding author in frequently cited orthopaedic journals. The final disposition term (acceptance or rejection) was recorded, and logistic regression was used to identify factors associated with acceptance for publication. RESULTS: Manuscripts from countries other than the United States or Canada were significantly less likely to be accepted (odds ratio, 0.51; 95% confidence interval, 0.28 to 0.92; p = 0.026). Factors positively associated with acceptance for publication were conflict-of-interest disclosure involving a nonprofit entity (odds ratio, 1.92; 95% confidence interval, 1.35 to 2.73; p < 0.001) and ten or more prior publications in frequently cited orthopaedic journals by the corresponding author (odds ratio, 2.01; 95% confidence interval, 1.33 to 3.05; p = 0.001). We did not find a significant association between acceptance and conflict-of-interest disclosure involving a for-profit company, sex of the corresponding author, or primary language. CONCLUSIONS: Manuscripts submitted to The Journal of Bone and Joint Surgery were more likely to be accepted if they were from the United States or Canada, reported a conflict of interest related to a nonprofit entity, or were authored by an individual with ten or more prior publications in frequently cited orthopaedic journals.
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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 | 0.025 | 0.211 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".