“Hotel Refuses Negro Nurse”: Gloria Clarke Baylis and the Queen Elizabeth Hotel
Bibliographic record
Abstract
On 2 September 1964, one day after the Act Respecting Discrimination in Employment was introduced in Quebec, Gloria Clarke Baylis, a British-trained Caribbean migrant nurse, inquired about a permanent part-time nursing position at the Queen Elizabeth Hotel (QEH). In response, she was told that the position had already been filled. Less than a year later, Gloria appeared as the key witness in Her Majesty the Queen, Complainant v. Hilton of Canada Ltd., Accused, to determine whether the QEH violated the new legislation. Drawing on excerpts from the court transcript, this article expands and complicates intersectionality as a theoretical framework to include other markers of difference. Critical to this discussion are two interrelated concerns: first, the connection between Gloria's experience at the QEH and Black women's historical relationship to nursing; second, how her subjectivity and identity influenced her decision to pursue the lawsuit.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.046 | 0.017 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".