High S. (2022) Deindustrializing Montreal: Entangled Histories of Race, Residence, and Class. McGill-Queen’s University Press
Bibliographic record
Abstract
Historian Steven High adds a beautifully nuanced account of Montreal to the literature on deindustrialization with his new book, Deindustrializing Montreal.High's expertise on deindustrialization, as evidenced in one of his prior books, Industrial Sunset: The Making of North America's Rustbelt, is applied to this study of two working-class neighborhoods in Montreal.He understands Montreal as a revivified, thriving city but one in which postindustrial development plays itself out unevenly across lines of class, race, and residence in two communities.High structures the book as a comparative study of Point Saint-Charles and Little Burgundy, two working-class neighborhoods in Southwest Montreal, one white and the other multiracial.The book takes the reader through waves of deindustrialization: the decline of the railroads with the growth of automobile culture; the closing of the Lachine Canal to ship traffic; and shutdowns, over decades, of the many factories along the banks of the canal.It also explores the histories of changing social policy in and around cities, exploring the impact of suburbanization, urban renewal, and gentrification on these neighborhoods.High's histories are supported by his in-depth, long-term, ethnographic work in these two communities.Steve High lives in Point Saint-Charles, has worked extensively with students and community partners in both neighborhoods, has collected oral histories, planned public events, conducted neighborhood walk-throughs and engaged with local institutions for more than 15 years.This long-term, deeply embedded research results in incredibly rich archival materials, including oral histories, photographs, and primary documents, most of which have been collected by High and his students.
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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 | 0.003 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.042 | 0.008 |
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".