Twelve Principles Trainees, PIs, Departments, and Faculties Can Use to Reduce Bias and Discrimination in STEM
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
There is an overwhelming amount of evidence demonstrating that people from marginalized groups, including women, racialized and Indigenous peoples, people with disabilities, immigrants, and LGBTQ+ individuals, continue to face substantial discrimination in STEM, manifested as both overt bias and unconscious bias. These biases result in discrimination against individuals in marginalized groups, and independent biases collectively contribute to a culture that systematically discriminates against people from marginalized groups. Representation from marginalized groups in postsecondary degrees in natural science and engineering has not substantially improved in over a decade. A set of 10 concrete principles are presented that trainees, principle investigators, departments, and faculties can use to enhance the participation and lived experiences of people in marginalized groups in STEM.
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 it