“Rewriting” Cultural Safety Within the Postcolonial and Postnational Feminist Project
Bibliographic record
Abstract
The concept of cultural safety, developed by indigenous nurses in the postcolonial climate of New Zealand, has not been widely examined in North America. In this article we explicate the theoretical and methodological issues that came to the forefront in our attempts to use this concept in our research with different populations in Canada. We argue that this concept prompts us to "think critically" about ourselves and our patients, and to be mindful of our own sociocultural, economic, and historical location. This critical reflection has implications for how we live, relate to one another, and practice in our various professional disciplines. On the basis of our findings, we discuss how the concept might be rewritten within a critical postcolonial and postnational feminist discourse.
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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 | 0.026 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.083 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".