Language as a proxy for race: Language and literacy and the nursing profession
Bibliographic record
Abstract
Defining a nurse as literate is disciplinary and contextual, linked to professional identity formation, and an issue impacting patient safety. Literacy and language proficiency are concepts assessed through examining skills in four pillars: reading, writing, speaking, and listening. This article explores how literacy is not only a practice issue but inextricably intertwined with issues of race, equity, diversity, and inclusiveness in our profession-both in regulatory policy and classroom pedagogy. In making the argument that language is a proxy for race, three cases of language and literacy will be presented. First, the deficit discourse of multilingual student struggle is stereotyped to the presence or absence of an accent, with multilingual student needs often treated homogeneously in disregard of population heterogenous abilities. Second, regulatory policies for language testing internationally educated nurses are discriminatory with testing context bearing little relationship to the language needs of nursing practice. Third, that the myth of "one standard English" results in racist evaluation practices of student academic performance. Recommendations are made for reframing how language and literacy are viewed in nursing education and regulation of practice with a focus on acknowledgment of one's personal relationship to racial issues and emphasizing the need for a change in mindset toward racialized multilingual students and writers.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".