Toward a Critical Theoretical Interpretation of Social Justice Discourses in Nursing
Bibliographic record
Abstract
Despite widespread appeals to social justice, nursing conceptions of this ideal have been critiqued as incomplete and inconsistent. With the aim of contributing to a critical dialogue on discourses of social justice in nursing, we explore contemporary theories of social justice and their move beyond a distributive paradigm, employing techniques of replication and critique of social justice discourses in nursing. We consider how postcolonial feminist theory can help us understand the relevance of more recent critical interpretations of social justice, particularly in reinterpreting and broadening nursing's individualistic focus on social justice so that due consideration and actions are directed toward the intersecting impact of historically and socially mediated conditions on health and human suffering.
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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 | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.005 | 0.038 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.006 | 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".