Authorship trends in the Campbell-Walsh-Wein Urology textbook
Bibliographic record
Abstract
Introduction: We aimed to examine temporal trends in author demographics of the Campbell-Walsh-Wein Urology textbook. Methods: Name, institution, specialty, and faculty rank were extracted for all authors (9th–12th editions). A survey was emailed to determine gender. When self-reported data were unavailable, demographics and urologic subspecialty were determined based on name and online biographies. Demographics of academic and practicing urologists were taken from the American Urological Association Census. Chi-squared tests were used for statistical analyses. Results: Across the 9th–12th editions, there were 1119 total authors; 597/627 (95.2%) unique authors were emailed (18 deceased; 12 missing emails) and 161 (27.0%) responded. The final cohort included 536 unique contributors after excluding authors who were not attending urologists. The percentage of women authors increased over time, from 3.4% in the 9th edition to 12.6% in the 12th edition. The gender distribution of authors in the 11th and 12th editions was comparable to the gender distribution of both academic and practicing urologists for the respective years (p>0.05). A greater proportion of men had attained the rank of professor at the time of authorship (50.2% of men vs. 13.5% of women), and female gender was significantly associated with lower academic rank (p<0.001). Conclusions: Women urologists represent a smaller but increasing presence as authors of Campbell-Walsh-Wein Urology, mirroring the demographics of academic and practicing urologists. Women authors tended to hold lower academic ranks than their male counterparts. These findings suggest that efforts to promote diversity in authorship have been successful, but there is still room for growth in academic advancement.
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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 | 0.003 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".