Jumpstarting STEM Careers
Bibliographic record
Abstract
A successful career in science, technology, engineering and mathematics (STEM) requires great education and research experiences as well as extensive training and preparation with dedicated mentors. Recent statistics show women received over 40% of all BA/BS degrees awarded by U.S. 4‐year colleges and universities in the life sciences and the proportion of women with doctorates has exceeded 40–60% in some fields. However, the proportion of women associate professors in the basic science departments of most universities is still below 30%, and the proportion with rank of full professor is only 20%. Therefore, the Central Arizona Chapter of the Association for Women in Science at ASU, in collaboration with colleagues from George Washington, Gallaudet and Ottawa Universities, has developed a program to help prepare graduate students, post docs and early faculty for careers in STEM. This NSF advance funded program tackles the problem of low career advancement of women and minorities by hosting career development seminars and workshops to provide training, mentoring and networking opportunities for graduate students and post docs. Such career training programs will help address the complex problem encountered by women and minorities and thus will aid in restoring a pipeline of diverse STEM professionals.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.055 | 0.014 |
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".