The mirage of scientific productivity and how women are left behind: the Colombian case
Bibliographic record
Abstract
Equity, diversity and inclusion (EDI) in the workforce are paramount for the betterment of the scientific endeavor. Colombia is a country with great scientific potential, but also multiple long-lasting socioeconomical difficulties. Here, we provide a quantitative analysis of the temporal trajectories of gender parity in scientific publishing in Colombia. Data was dissected based on education level, researcher’s rank and research area, in order to elucidate differential patterns of scientific publishing. We controlled for gender-based differences in number of researchers by quantifying per capita scientific productivity. Our results show widespread gender disparity in scientific publishing persistent across time. Gender-based differences in per capita scientific publishing indicate that gender disparity persists even after controlling for differences in the number of researchers. Temporal trajectories revealed a decrease in women publishing in the medical sciences and a widening of the per capita publishing gender gap. Women senior researchers and women researchers with doctoral degrees had the lowest publishing participation within their group, suggesting access to postgraduate education or entering the workforce in themselves do not prevent women from being underrepresented. We highlight the need to understand the problem of underrepresentation in science and possible ways to address it beyond increasing the number of women researchers.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".