From opportunity gap to opportunity yield: The benefits of out-of-school authentic mentored research for youth from historically marginalized communities in STEM
Bibliographic record
Abstract
Our longitudinal, mixed methods study explores the experiences of over five hundred youth in long-term mentored research experiences outside of school, paired with data on their reports of plans to pursue STEM. Our participants, youth from historically marginalized communities, represent the most promise for diversifying STEM: 81% are students of color, and almost half are multilingual. This paper shares an analysis of a cross-section of quantitative data collected from this large-scale study as well as qualitative data in the form of participant interviews. Drawing from our quantitative data, we find that in stark contrast to the opportunity gaps that youth like our participants encounter, participating in out of school research generates a ‘yield’ of opportunities to engage in science practices–significantly more than in school– and to contribute meaningfully to a science community of practice. Our qualitative data suggests that this ‘opportunity yield’ may also contribute to their continued pursuit of STEM. Taken together, these findings underscore the critical role that learning in out-of-school mentored research settings can play for students revealing its important, complementary role in a STEM ecosystem.
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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 | 0.020 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.002 | 0.025 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".