The influence of democratic racism in nursing inquiry
Bibliographic record
Abstract
Neoliberal ideology and exclusionary policies based on racialized identities characterize the current contexts in North America and Western Europe. Nursing knowledge cannot be abstracted from social, political and historical contexts; the task of examining the influence of race and racial ideologies on disciplinary knowledge and inquiry therefore remains an important task. Contemporary analyses of the role and responsibility of the discipline in addressing race-based health and social inequities as a focus of nursing inquiry remain underdeveloped. In this article, we examine nursing's engagement with ideas about race and racism and explore the ways in which nursing knowledge and inquiry have been influenced by race-based ideological discourses. Drawing on Henry and Tator's framework of democratic racism, we consider how strategic discursive responses-the discourses of individualism, multiculturalism, colour-blindness, political correctness and denial-have been deployed within nursing knowledge and inquiry to reinforce the belief in an essentially fair and just society while avoiding the need to acknowledge the persistence of racist discourses and ideologies. Greater theoretical, conceptual and methodological clarity regarding race, racialization and related concepts in nursing inquiry is needed to address health and social inequities.
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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 | 0.073 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.019 | 0.111 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".