How Can Outreach Foster Further Interest in Stem and Eventually Lead to Careers in STEM?
Bibliographic record
Abstract
Numerous universities, colleges, and STEM-focused organizations co-create outreach activities with secondary education institutions by connecting the work-context with school-science, with the aim to inspire students and motivate them to consider a career in STEM. Although many such activities are being offered, little is known about their actual influence and outcomes. In this article, I approach the experts from STEM outreach field, with backgrounds ranging from not-for-profit science centers, through industry, research institutes, and universities, will address their approach to measuring what impact their STEM outreach programs had on program participants. At the same time, they will address what role equity, diversity and inclusion plays in their programs, how they achieve diversity of participants and in what ways it influences quality of experience for the program participants. Through this article I am bringing together the thoughts of scientists, industry experts, and programs leaders on how outreach can motivate students to pursue careers in science.
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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 | 0.014 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".