Protect US racial affinity groups
Bibliographic record
Abstract
On its first day, the Trump administration released several executive orders terminating diversity, equity, and inclusion (DEI) programs, calling them “illegal,” “immoral,” and “discriminatory” (1, 2). DEI programs include racial affinity groups, which counter the systemic barriers to inclusion and advancement that Black, Indigenous, and People of Color (BIPOC) face in science, technology, engineering, and mathematics (STEM) disciplines (3, 4). These organizations provide a welcoming space for underrepresented scientists to give and receive culturally aware mentorship (5). Given that diverse teams produce more innovative science (6), racial affinity groups benefit not only BIPOC scientists but also their employers and the public. Racial affinity groups do not violate US antisegregation or antidiscrimination laws; they enable equitable access to resources that support academic advancement for all. To protect scientists and scientific output, US stakeholders must work to protect affinity groups from government interference.
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 | 0.015 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.029 | 0.008 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.092 | 0.046 |
| Insufficient payload (model declined to judge) | 0.018 | 0.011 |
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".