Sex and authorship in global cancer research
Bibliographic record
Abstract
Introduction: Research is an essential pillar of cancer control and key in shaping regional cancer control agendas. Imbalances in science and technology in terms of lack of female participation have been well documented. However, there is little evidence about country-level female participation in cancer research. Methodology: Through a complex filter, cancer research papers were identified and grouped by countries and sex of the first and last authors of each paper and analysed by the percentage of females in these positions alongside other parameters. Results: Our analysis of 56 countries' outputs, in 2009, revealed that females were the first authors in 37.2% and last authors in 23.3% of papers. In 2019, females were the first author in 41.6% and last author in 29.4% of papers. Females increased as first authors by 26%, and as last authors by 12% between these two time periods. The top performing countries in terms female/male parity for first or last authorship were in Eastern and Southern Europe as well as Latin American countries.From 2009 to 2019, the highest proportion of females as first and last authors were from low-income and middle-income countries in Latin America and Eastern Europe.Females were more likely to publish in lower impact journals and were less likely to be cited compared to males. Conclusions: Globally, progress in female's authorship in oncology research has been uneven. More research is needed to understand the reasons behind this. Advancing diversity and equity in research leadership and authorship will be essential to address the complex challenges of cancer globally.
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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 | 0.016 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".