#WomenWhoCurie: Leveraging Social Media to Promote Women in Radiation Oncology
Bibliographic record
Abstract
The proportion of female trainees in radiation oncology has generally declined despite increasing numbers of female medical students; as a result, radiation oncology is among the bottom 5 specialties in terms of the percentage of female applicants. Recently, social media has been harnessed as a tool to bring recognition to underrepresented groups within medicine and other fields. Inspired by the wide-reaching social media campaign of #ILookLikeASurgeon to promote female physicians, members of the Society for Women in Radiation Oncology penned a new hashtag and launched the #WomenWhoCurie social media campaign on Marie Curie's birthday November 7th, as part of their strategy to raise public awareness. From November 6, 2018 until November 10, 2018, the #WomenWhoCurie hashtag delivered 1,135,000 impressions, including 408 photos from all over the world including United States, Spain, Canada, France, Australia, Ireland, the United Kingdom, Mexico, Japan, the Netherlands, India, Ecuador, Panama, Brazil, and Nigeria. Alongside continued gender disparity research, social media should continue to be used as a tool to engage the community and spur conversations to formulate solutions for gender inequity.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".