Female researchers are under-represented in the Colombian science infrastructure
Bibliographic record
Abstract
Worldwide women have increased their participation in STEM, but we are still far from reaching gender parity. Although progress can be seen at the bachelor's and master's level, career advancement of women in research still faces substantial challenges leading to a 'leaky pipeline' phenomenon (i.e., the continuous decrease of women's participation at advanced career stages). Latin America exhibits encouraging rates of women participation in research, but the panorama varies across countries and stages in the academic ladder. This study focuses on women's participation in research in natural sciences in Colombia and investigates career progression, leadership roles, and funding rates by analyzing data on scholarships, grants, rankings, and academic positions. Overall, we found persistent gender imbalances throughout the research ecosystem that were significant using classical statistical analyses. First, although women constitute >50% graduates from bachelors in natural sciences, <40% of researchers in this field are female. Second, women win <30% of research grants, and in turn, their scientific productivity is 2X lower than that of men. Third, because of the less research funding and output women have, their promotion to senior positions in academic and research rankings is slower. In consequence, only ~25% of senior researchers and full professors are women. Fourth, the proportion of women leading research groups and mentoring young scientist in Colombia is <30%. Our study deepens our understanding of gender gaps in STEM research in Colombia, and provides information to design initiatives that effectively target gender disparities by focusing on key areas of intervention, and then gradually building up, rather than tackling structural inequities all at once.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".