From protest to progress through partnership with students: Being human in STEM (HSTEM)
Bibliographic record
Abstract
In Fall 2015, Amherst College students held a four-day sit-in in unity with student protests occurring all over the United States highlighting barriers to inclusion of underrepresented and marginalized students.Following appeals for action, students partnered with faculty and staff in science, technology, engineering, and mathematics (STEM) to develop the Being Human in STEM (HSTEM) Initiative. HSTEM involves exploring past diversity and inclusion efforts in STEM, sharing one’s own experiences in STEM with others, and developing student-driven projects to improve belonging in STEM. In this student, faculty, and staff co-authored paper, we describe the origin of HSTEM; share student, faculty and staff reflections on our experiences with HSTEM; and present two inquiry projects examining HSTEM impact. We discuss lessons learned and recommendations for diversity and inclusion efforts in higher education, both in and beyond STEM, emphasizing the power of an initiative that was originated by and remains driven by student partners.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.016 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.002 | 0.035 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 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".