WeWhoCurie: An Initiative to Advocate for Those Underrepresented in Radiation Oncology
Bibliographic record
Abstract
Purpose: An initiative to advocate for those underrepresented in radiation oncology. Methods and Materials: Inspired by the success of the #ILookLikeAnEngineer and #ILookLikeASurgeon campaigns, this initiative aimed to break down stereotypes in traditionally male-dominated fields. In honor of Marie Curie's birthday, on November 7, 2018, the Society for Women in Radiation Oncology launched a social media campaign called #WomenWhoCurie day. However, as the popularity of the social media campaign increased, it become evident that members of the wider radiation community, in particular women of color, nonbinary and transgender people did not feel supported by the #WomenWhoCurie movement. In November 2021, after consultation with diversity and inclusion leaders and members of other national radiation oncology organizations, Society for Women in Radiation Oncology launched #WeWhoCurie alongside the #WomenWhoCurie campaign for women and gender minorities in radiation oncology. Radiation oncologists, physicists, dosimetrist, therapists, nurses, and other professionals from around the world gathered and shared photos and social media posts throughout the day on multiple platforms including Facebook, Instagram, and Twitter. Results: In the year #WeWhoCurie, #WomenWhoCurie, #_______ WhoCurie campaign launched, we saw an increase in participation across the globe from 9 countries: the United States, Canada, Mexico, Brazil, Italy, Spain, China, New Zealand, and Australia. There were over 720 tweets contributing to the campaign with over 2000 messages, representing 3,365,444 "potential impacts", or the number of times someone saw the hashtag. Conclusions: " patients with cancer and conducting cutting edge research to improve cancer care across the globe. As an organization we believe adding our voices to the masses will foster a culture of inclusion for everyone. Afterall, what good is the practice of radiation oncology if all are not equally welcome?
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.030 | 0.006 |
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".