Making and defending intimate spaces : white waitresses policed in Vancouver's Chinatown cafes
Bibliographic record
Abstract
In mid-1930s Vancouver, city authorities launched a campaign to ban white waitresses from Chinatown cafes. Canadian historians have overlooked this campaign because of the tendency to treat the Chinese in Canada as a separate history from working women and to focus on discourse analysis rather than experience. This obscures the importance of sexuality and cross-racial interaction to the lives of both Chinese “bachelors” and white working women in Canada. This paper shows how white waitresses, Chinese restaurant owners, and Chinese patrons created and defended a social space of cross-racial intimacies in Vancouver’s Chinatown cafes. By examining a variety of sources, including mainstream and labour newspapers, mayor’s and police records, oral histories, and Chinese-language newspapers, this paper considers the perspectives of the four groups involved in the campaign. City authorities constructed the cafes as immoral spaces, where white waitresses were enticed into prostitution by Chinese men. In the name of protecting white womanhood, they drew a gendered and racial line around Chinatown. Despite policies of racial and gender equality, labour organizations also viewed the campaign through this lens of morality. For the white waitresses and Chinese customers, on the other hand, these cafes opened up a social space to explore cross-racial intimacies. In the cafes, they flirted, formed friendships, and began sexual relationships. The Chinese “treated” the waitresses to dinner, gifts, or money in exchange for sexual intimacy. Some of these intimacies were purely functional, while others developed into relationships that fulfilled mutual interests, needs, and desires. Through these intimate practices, they created choices and opportunities not available outside of Chinatown. The ban forced the Chinese, and especially the white waitresses, to become self-reflective about their experience in the cafes. The Chinese condemned the ban as racial discrimination. Fifteen white waitresses marched on city hall, where they defended their rights as workers, their respectability, and their Chinese employers. The waitresses articulated why the Chinatown cafes held value in their lives and in Vancouver. They had lost their jobs and their reputations, but they took a political stand.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.036 | 0.010 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".