Female Authorship in Radiology: Trends in the Past Decade in CARJ
Bibliographic record
Abstract
Purpose: To identify trends in female authorship in the Canadian Association of Radiologists Journal (CARJ) from 2010 to 2019. Methods: We retrieved papers published in the CARJ over a 10-year period, and retrospectively reviewed 602 articles. All articles except editorials and advertisements were included. We categorized the names of the first and last position authors as female or male and excluded articles that had at least one author of which gender was not known. We compared the trends in the first and last position authors of the articles from 2010 to 2019. For statistical analysis, logistic regression was performed with reported odds ratios (ORs), and a P value of <.05 was defined as statistically significant. Results: Five hundred thirteen articles met inclusion criteria. Among them, 23 articles with a single author were classified as having only a first author. 39.8% (204/513) of first authors were female and 26.9% (132/490) of last authors were female. There has been an overall temporal increase in the odds of both the first and last author being female in CARJ publications (OR: 1.11, P = .034). Similarly, the odds a CARJ publication’s first author being female increased over time (OR: 1.07, P = .033). Female last author did not predict female first author (OR: 1.48, P = .056). There was no association identified between female last author and year of publication (OR: 1.04, P = .225). Conclusion: There has been an overall increase in engagement of female authorship in CARJ.
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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 | 0.004 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".