Nurse Angélique : revisioning French Catholic nursing history as an ethical intervention in contemporary Canadian nursing practice
Bibliographic record
Abstract
The social justice mandate of Canadian nursing ethics drives the profession to intervene on systems of inequity, like racism. Canadian nurses from the Black diaspora repeatedly report experiencing racial discrimination in their everyday practice. This occupational violence is sustained by Canadian nursing’s reliance and perpetual reproduction of the ideologies of whiteness and femininity. The ethics of Canadian nursing is demonstrated to be contrary to its actions as evidenced by the experiences of Black nurses. Those concerns are not new, just like Black folks in Canada are not new. In 1734, a Black woman known as Angélique was convicted of arson by the government of New France. For her crime, she was publicly executed in the town centre of Montréal. Present-day scholars recognize the significance of the court record of Angélique’s trial: those documents are primary historical evidence that slavery was practiced in Canada. In this a/r/tographic exploration, openings are formed. Openings are emotional, uncomfortable, messy, and relational. The connections between Angélique, as a history of resistance, and contemporary Canadian nursing practice are reconnoitred through the living-inquiry of a/r/tography. The artistic component reimagined Angélique as a modern Covid-19 nurse while asking what makes Blackness so surprising in Canadian nursing? The inquiry generated from the artmaking involved reinterpreting Angélique’s story vis-à-vis early French Catholic nursing history and analysing how the current scholarship about the celebrated genesis of Canadian nursing naturalizes ideologies of whiteness and femininity. The revisioning of our shared nursing history through a Black feminist intersectional lens, with sexuality a key vector of analysis, produced a sweaty concept of “the Canadian nurse”. The reader is invited to negotiate their own meaning about these openings with no solutions, recommendations, or strategies are provided. The future of nursing needs to be anti-racist [+ anti-colonial] if it wants to realign itself with disciplinary obligations towards social justice. The future of nursing must be accountable to its past. Embracing our reinterpreted history despite its shattering of the benevolence of the Canadian nurse. Finally, the future of nursing benefits from being imagined beyond the current limits of what is considered “good” and “moral” nursing.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.042 | 0.036 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".