A Fine Balance: How Authors Strategize Around Journal Submission
Bibliographic record
Abstract
PURPOSE: Publishing in peer-reviewed journals is essential for medical education researchers. Competition remains fierce for top journals, and authors are advised to consider impact factor (IF), audience, and alignment of focus. However, little is known about how authors balance these factors when making submission decisions. The authors aimed to explore decision making around journal choice. METHOD: Using constructivist grounded theory, the authors conducted and analyzed 27 semistructured phone interviews (August-November 2016) with medical education researchers. Participants were recruited from a larger study, and all had presented abstracts at medical education meetings in 2005 or 2006. RESULTS: When deciding where to submit an article, participants weighed a journal's IF and prestige against other factors, such as a journal's vision and mission, finding the right audience, study-specific factors including perceived quality of the work, and the peer review process. The opportunity cost of aiming high and risking rejection was influenced by career stage and external pressures. Despite much higher IFs, clinical journals were viewed as less desirable for establishing legitimacy in the medical education field and were often targeted for less novel or rigorous work. Participants expressed dissatisfaction with peer review in general, citing overly critical and poorly informed reviewers. CONCLUSIONS: Authors strategize around a particular article's submission by attempting to balance many interrelated factors. Their perceptions that high-IF clinical journals are viewed as less prestigious in this field can lead to publication strategies running counter to advice given to junior faculty. This has implications for mentorship and institutional leadership.
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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 | 0.111 | 0.359 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.031 | 0.016 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".