Bibliographic record
Abstract
The 1961 case of King v Barclay is something of a footnote in the history of discrimination against Black Canadians. If it is cited at all, it is usually cited alongside the more famous racism cases, such as Christie v York, as proof of the widespread nature of racism in Canada. In this paper, I re-read the trial decision and examine the original case file to show that the facts of King and the racism in the case are more complex than usually realized. King emerged out of a series of errors from both King and Barclay’s Motel which resulted in the latter assuming, or seeming to assume, that King wished to visit two prostitutes working out of the motel. For obvious reasons, however, Barclay’s Motel could not state such an allegation explicitly as that would have been tantamount to admitting that they knew the women in question were prostitutes. In order to recapture the full legal and social contexts of King this paper examines both the history of racial discrimination in public accommodations and the longstanding struggle to prevent prostitutes from using such accommodations to ply their trade. The paper also argues that King’s legal action, even though he lost in court, was ultimately successful in that it prompted a legislative amendment, which removed the technicality upon which King turned.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.037 | 0.026 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 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".