“In the end, we had to leave”: Truth-telling to unsettle whiteness in nursing academia
Bibliographic record
Abstract
Nursing is renowned for its high ethical standards and is considered one of the most trusted professions globally, yet it has deep historical ties to Eurocentric and white supremacist ideologies. These entrenched ideologies in nursing raise significant concerns regarding equity, diversity, and inclusion within the profession as they shape nursing education, research, and practice. Western nursing institutions are deeply engrained in a system designed to center and uphold whiteness, which frequently serves to safeguard dominant groups in power while detrimentally affecting faculty from underrepresented backgrounds. Consequently, faculty members from underrepresented groups depart academia due to systemic racism and inadequate institutional accountability and support. To decenter whiteness in nursing, we have shared our experiences to underscore how systems of oppression marginalize underrepresented faculty in nursing academia.
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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 | 0.046 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.045 | 0.082 |
| Scholarly communication | 0.021 | 0.022 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.008 | 0.018 |
| Insufficient payload (model declined to judge) | 0.005 | 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".