Little Burgundy: The Interwoven Histories of Race, Residence, and Work in Twentieth-Century Montreal
Bibliographic record
Abstract
Until the 1950s, most black men in Montreal worked for the railway companies as sleeping car porters, dining car employees, and red caps. The city’s English-speaking black community took root in Little Burgundy because it was close to Windsor and Bonaventure train stations. The area between Saint-Henri and Griffintown, north of the Lachine Canal, in the city’s Southwest Borough, was once known by many names. “Little Burgundy” was invented in the 1960s by city officials to describe their urban renewal plans for the area. If employment mobility was foundational in making this community, it proved just as central in its unmaking in the 1960s and 1970s. The shift from trains to cars and trucks had a two-fold impact on Little Burgundy. First, employment levels collapsed with the decline of passenger train travel, leaving many black men unemployed. Then the state built a highway through the neighbourhood to facilitate the mobility of mainly white suburban workers and consumers making their way to the central city. Next, the neighbourhood was “renewed” on a massive scale. What followed were years of dislocation and crisis. Much of the black community was dispersed as a result. It was no coincidence. The radical restructuring of North American cities disproportionately affected racialized minorities and poor whites. What was different here was that the area’s reputation for being the birthplace of black Montreal emerged after the community had been largely dispersed by urban renewal.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.028 | 0.014 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".