Journal editors: How do their editing incomes compare?
Bibliographic record
Abstract
Background: The work of journal editors is essential to producing high-quality literature, and editing can be a very rewarding career; however, the profession may not be immune to gender pay gaps found in many professions and industries, including academia and clinical medicine. Our study aimed to quantify remuneration for journal editors from core clinical journals, determine if a gender pay gap exists, and assess if there are remuneration differences across publishing models and journal characteristics. Methods: We completed an online survey of journal editors with substantial editing roles including section editors and editors-in-chief, identified from the Abridged Index Medicus “Core Clinical” journals in MEDLINE. We analyzed information on demographics, editing income, and journal characteristics using a multivariable partial proportional odds model for ordinal logistic regression. Results: There were 166 survey respondents (response rate of 9%), which represented editors from 69 of 111 journals (62%). A total of 140 fully completed surveys were analyzed (95 males and 45 females); 50 (36%) editors did not receive remuneration for editorial work. No gender pay gap and no difference in remuneration between editors who worked in subscription-based publishing vs. open access journals were detected. Editors who were not primarily health care providers were more likely to have higher editing incomes (adjusted odds ratio [OR] 2.96, 95% confidence interval [CI] 1.18-7.46). Editors who worked more than 10 hours per week editing earned more than those who worked 10 hours or less per week (adjusted OR 16.7, 95%CI 7.02-39.76). Conclusions: We were unable to detect a gender pay gap and a difference in remuneration between editors who worked in subscription-based publishing and those in open access journals. More than one third of editors surveyed from core clinical journals did not get remunerated for their editing work.
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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 | 0.013 | 0.138 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".