Empowering Female High School Students for STEM Futures: Career Exploration and Leadership Development at Scientella
Bibliographic record
Abstract
Women remain underrepresented in STEM fields, with a major STEM pipeline leakage occurring between high school and post-secondary education. Past research suggests that providing female high school students with opportunities for problem-solving, prosocial behaviors and working towards authentic communal goals can improve their perceptions of STEM and the attractiveness of STEM careers. Building on this prior research, we investigate Scientella, a US-based, student-run organization that provides out-of-school consulting projects, mentorships and webinars to female high school students. Drawing on the direct experiences of Scientella’s co-founders and analyzing program survey data, we explore how Scientella provides these opportunities, the benefits realized by students, and the challenges faced by the organization. Survey data show that involvement in Scientella’s activities provides students with benefits related to STEM career exploration and counter-stereotypical STEM skill development, including career discovery, industry engagement and practical experience, and the development of collaboration, communication and social research skills. The admissions of Scientella student leaders to STEM majors in selective US colleges indicates the promise of Scientella’s approach, and that providing students with opportunities to engage in STEM-themed career exploration and leadership development could be an effective strategy to increase female STEM participation in post-secondary education and the pursuit of subsequent career opportunities.
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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 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".