BUILDING A NETWORK TO SUPPORT GIRLS AND WOMEN IN SCIENCE, TECHNOLOGY, ENGINEERING, AND MATHEMATICS
Bibliographic record
Abstract
Women today constitute over half of the U.S. population and almost half of its overall workforce, yet they make up less than a quarter of the science and engineering workforce. Many historical and social factors contribute to this discrepancy, and numerous individual, institutional, and governmental attempts have been made to redress it. However, many of the efforts to promote, include, and engage girls and women in science, technology, engineering, and mathematics (STEM) education and professions have been made in isolation. At Kansas State University, the authors have begun a systemic effort to increase the participation of girls and women in STEM. This article describes the creation and initial activities of a network of partners that includes universities, school districts, corporations, governmental agencies, and nonprofit organizations, assembled under the aegis of a project supported by funding from the National Science Foundation.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".