An investigation into gender distributions in scholarly publications among dental faculty members in Iran
Bibliographic record
Abstract
BACKGROUND: Research on gender inequality is crucial as it unveils the pervasive disparities that persist across various domains, shedding light on societal imbalances and providing a foundation for informed policy-making. AIM: To investigate gender differences in scientometric indices among faculty members in dental schools across Iran. This included overall data and speciality-specific data. METHODS: The publication profiles of academic staff in all dental schools were examined using the Iranian Scientometric Information Database (ISID, http://isid.research.ac.ir). Variables analyzed were working field, academic degree, the total number of papers, papers per year, total number of citations, percentage of self-citation, h-index, g-index, citations per paper, gender, university type, number of years publishing, proportion of international papers, first-author papers, and corresponding-author papers. Mann-Whitney and Kruskal-Wallis nonparametric tests were used to analyze the relationship between background characteristics and scientometric indicators. The extracted data were analyzed using R v4.0.1. RESULTS: The database included 1850 faculty members, of which about 60% (1104 of 1850) were women. Men (n = 746) had a higher number of papers (6583 vs. 6255) and citations (60410 vs. 39559) compared with women; 234 of the 376 faculty members with no papers were women. Almost half of the women (N = 517 of 1104) were in Type 2 universities, and nearly half of the men (N = 361 of the 746) were faculty members at Type 1 universities (Type 1 universities ranking higher than Type 2 and 3 universities). The medians of scientometric indices were higher in men, except for self-citation percentage (0 (IQR = 2) vs. 0 (IQR = 3), P = 0.083), international papers percentage (0 (IQR = 7.5) vs. 0 (IQR = 16.7), P<0.001). The proportion of corresponding-author papers was more than 62% higher in women (25 (IQR = 50) vs. 15.4 (IQR = 40), P<0.001). Men had a two-fold higher median h-index (2 (IQR = 4) vs. 1 (IQR = 3), P<0.001). Restorative dentistry and pediatric dentistry had the highest men-to-women ratios (1.5 for both). Dental materials and oral and maxillofacial surgery showed the lowest men-to-women ratios (0.42 and 0.5, respectively). CONCLUSIONS: Women made up the majority of dental faculty members in Iran. Nevertheless, men showed better scientometric results in several significant indices. Having identified scientometric information reflecting differences across faculty members, further research is now needed to better understand the drivers of these differences.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".