High S. (2022) Deindustrializing Montreal: Entangled Histories of Race, Residence, and Class. McGill-Queen’s University Press
Why this work is in the frame
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Bibliographic record
Abstract
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it