Toward Sustained Cultural Change through Chemistry Graduate Student Diversity, Equity, and Inclusion Communities
Bibliographic record
Abstract
Evidence from the past several decades has repeatedly found that the chemical enterprise presents systemic barriers for people with marginalized identities. Initiatives and actions that remove systemic barriers and support the success of all students, especially those from equity-deserving groups, are essential. However, fostering a culture of inclusion requires actions that focus on not only immediate outcomes but also sustained and lasting impacts. Chemistry graduate student communities focused on diversity, equity, inclusion, and respect (DEIR) can contribute to the transformation of our shared chemistry community into one where DEIR principles are foundational. Chemistry graduate student DEIR communities (CGDEIRCs) are learning communities in which students from equity-deserving groups in chemistry gain various forms of cultural wealth that support their attainment of cultural capital. By supporting students’ development of skills, networks, and resources to attain success, over time, CGDEIRCs may contribute to both short- and long-term DEIR cultural shifts in the broader chemistry community.
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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 | 0.027 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.024 |
| Research integrity | 0.002 | 0.002 |
| 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".