Responses to 10 common criticisms of anti-racism action in STEMM
Bibliographic record
Abstract
The wrongful murders of Black individuals during 2020 (including George Floyd, Breonna Taylor, Ahmaud Aubery, and others), compounded by a long history of similar incidents, inspired protests around the world against racism and police brutality. The growing anti-racism movement sparked conversations within science, technology, engineering, mathematics, and medicine (STEMM) surrounding ways to combat racial bias in our respective fields. A spotlight was placed on the discriminatory history of scientific research and medical practice, as well as the problematic modern-day policies that perpetuate the lack of racial diversity and equity in STEMM.\n\n\n\nWhile observing and participating in recent discussions about the racism that pervades institutions, departments, and scientific discourse, we have noticed a set of standard arguments against anti-racism action within STEMM. Ten of these arguments are laid out in this manuscript and paired with evidence-based counterarguments. Notably, while this manuscript is primarily centered around a United States perspective, most of our arguments and suggested actions remain applicable to other countries as well. It is crucial for a STEMM anti-racism movement to extend beyond national borders, reflecting the international nature of scientific research and collaboration.\n\n\n\nThis team of authors represents a collaboration between scientists from historically marginalized groups and their allies. By compiling published academic literature, we hope to directly confront racist ideology in STEMM with evidence-based arguments while simultaneously amplifying the research and perspectives of scholars of color. Our broad goal in articulating this information is to facilitate more productive conversations (and, in turn, tangible systemic changes) toward addressing racial discrimination within STEMM.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".