Ten simple rules for succeeding as an underrepresented STEM undergraduate
Bibliographic record
Abstract
Undergraduate students from underrepresented backgrounds (e.g., Black, Indigenous, and people of color [BIPOC], members of the Deaf community, people with disabilities, members of the 2SLGBTQIA+ community, from low-income backgrounds, or underrepresented genders) continue to face exclusion and marginalization in higher education. In this piece, authored and edited by a diverse group of Science, Technology, Engineering, and Mathematics (STEM) scholars, we present 10 simple rules for succeeding as an underrepresented STEM undergraduate student, illuminating the "hidden curriculum" of STEM specifically as it relates to the underrepresented undergraduate experience. Our rules begin by encouraging students to embrace their own distinct identities and scientific voices and explain how students can overcome challenges unique to underrepresented students throughout their undergraduate degrees. These rules are derived from a combination of our own experiences navigating our undergraduate STEM degrees and the growing body of literature on improving success for underrepresented students.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".