Assessing gage: an online tool for improving gender visibility in STEMM
Bibliographic record
Abstract
Women continue to be underrepresented and less visible in the fields of science, technology, engineering, mathematics, and medicine (STEMM). 500 Women Scientists created and launched in January 2018 a global (>140 countries to date), online, open-access directory of women in STEMM fields. This directory—recently renamed gage—now also includes gender diverse persons (i.e., additional underrepresented genders) in STEMM fields. The purpose of the directory is to make these scientists’ expertise easier to locate and access for conference organizers, journalists, policy makers, educators, and others. Here, we undertake an assessment of the directory using surveys, Google Analytics, and focus groups to understand its efficacy and direction to date and identify future improvements we pledge to undertake. Through this assessment—conducted externally and in accordance with privacy protocols by Concolor Research—we identified who and how people are using our directory, why people signed up to be a resource, and areas for improvement. Through such assessment, we can learn how to enhance the directory’s efficacy and our broader efforts to boost the visibility of underrepresented people in STEMM.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".