Sound, Deindustrialization, and Gentrification: \nThe Changing Aural Landscape of Pointe-Saint-Charles
Bibliographic record
Abstract
The development of the Grand Trunk Railway of Canada during the 1850s established Pointe-Saint-Charles as a critical rail hub and positioned Montreal as the nexus for Canada’s growing railway system. The rail yards and surrounding industries attracted thousands of workers and their families. The sounds of engines, factory whistles, and the shunting noises of train cars being latched together formed a distinctive new soundscape that would come to define Pointe-Saint-Charles. This blue-collar community would continue to thrive as a seat of industrial activity in Montreal until the Saint Lawrence Seaway opened in 1959, which allowed much commercial traffic to bypass Pointe-Saint-Charles. \nThe ensuing years were a time of change in this area as the neighborhood transitioned from an almost exclusively working-class enclave to a mixed, low-income and middle-class population. In 2015, in response to perceived demands from local residents, the Agence métropolitaine de transport announced that it would build a sound berm along rue de Sébastopol to dampen the sound of train traffic. This proposal was met with confusion by many long-time residents who felt that the train sounds were a vestige of Pointe-Saint-Charles’ working-class past and a defining part of the neighborhood. The berm, a large and imposing physical barrier, has profoundly altered the landscape of rue de Sébastopol. Its physical presence is in conflict with the existing architecture and cultural landscape of the street and the neighborhood. In this thesis, I explore the spatial, sensorial (the importance of engaging the senses within a broad consideration of a place), and symbolic effect of the berm on the community and aural landscape of Pointe-Saint-Charles as a window into the profound impact of the aural landscape on its environment.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.019 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.004 |
| 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".