Bibliographic record
Abstract
Deindustrializing Montreal is the culmination of fifteen years of researching my way into my adopted home in the city's deindustrialized, but now gentrifying, Southwest Borough.Even more than usual, the pages that follow are the result of a winding road of research and teaching, and not the outcome of a project with a single purpose and fixed end point.These wider engagements don't always show up in the endnotes, but they deepened my knowledge of Point Saint-Charles and Little Burgundy, the two neighbourhoods at the centre of this study, and opened up the research process to student participation as well as public engagement.Over the years, I also learned an incredible amount from graduate students under my cosupervision who have focused on the working-class neighbourhoods adjoining the Lachine Canal, including two doctoral students (Fred Burrill and Shauna Janssen), five master's students (Jessica Mills, Samah Affan, Simon Vickers, Tanya Steinberg, and Kelann Currie-Williams), and two undergraduate thesis students (David Sworn and Abbey Mahon). 1 Joyce Pillarella has likewise been instrumental in teaching me about the Italian-Canadian enclave in the Ville Émard neighbourhood.Fourteen undergraduate and graduate courses taught by myself or others between 2014 and 2019 were implanted in Little Burgundy or Point Saint-Charles, which face each other across the Lachine Canal.My years of co-teaching with Ted Little (theatre), Cynthia Hammond (art history), and Kathleen Vaughan (art education) as part of the Right to the City initiative represent one of the highlights of my teaching career thus far.Over three years, we tethered our courses so that they would all be in overlapping time slots, and we based ourselves at Share the Warmth, a
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.460 | 0.303 |
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".