Inspiring Girls and their Female After School Educators to Pursue Computer Science and other STEM Careers
Bibliographic record
Abstract
The dearth of women, particularly women of color, in science, technology, engineering, and math (STEM) fields is a well-known problem (National Academy of Sciences, 2010) . After school and summer programs exist to encourage girls and young women to study and pursue careers in these fields ( National Research Council , 2009) . From evaluations of these programs, we can learn what program participants are gaining, and if longer-term studies are conducted, we might see that these girls are pursuing college majors in STEM or entering the workforce as computer scientists, software developers, or electrical engineers. But what of the educators who lead the programs? Does teaching girls about STEM change educators’ views of STEM learning and careers? In this paper, we look at findings from one program, a computer science after school and summer program for middle school girls implemented in the United States and Canada, focusing on the program leaders to see if they experience changes in their views of STEM and their interest in pursuing STEM careers. These leaders are generally young adult women of color with little background in STEM who are considering next steps in their own careers. Our mixed-methods approach includes surveys, interviews, and observations as data sources.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".