Towards equal representation - A bibliometric analysis of authorships in Laboratory Medicine and Clinical Chemistry from the United States, Canada, and Europe (2005–2022)
Bibliographic record
Abstract
Objectives: Although diversity has been demonstrated to benefit research groups, women remain underrepresented in most scientific disciplines, including Laboratory Medicine and Clinical Chemistry. In order to promote diversity and equality in scientific communities, understanding the gender distribution of authorship is crucial. Methods: This study included a total of 30,268 Web of Science-listed Clinical Chemistry and Laboratory Medicine publications from the United States of America, Canada, and the member countries of the European Federation of Clinical Chemistry and Laboratory Medicine from 2005 to 2022. In addition to the publication productivity of female and male authors over time, gender-specific publication characteristics and country-specific gender distributions of authorships were examined. Results: Overall, publications with female first authors increased by 49 % between 2005 and 2022, averaging 42 % female first authors. Eastern Europe (60 %) and Southern Europe (51 %) had particularly high proportions of female first authors. While female last authorship was the most predictive of female first authorship, with an odds ratio of 2.01 (95 % CI: 1.91-2.12, p < 0.001), only 27 % of last authors were female. Moreover, citation rate was not predictive of female first or last authorship. Conclusion: Authorship in Clinical Chemistry and Laboratory Medicine is moving towards gender parity. This trend is more pronounced for first authors than for last authors. Further research into the citations of female authors in this discipline could be a starting point for increasing the visibility of women researchers in science. Moreover, geographical differences may provide opportunities for future research on gender parity across disciplines.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.055 | 0.103 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".