The Creative Destruction of Montreal: Street Widenings and Urban (Re)Development in the Nineteenth Century
Bibliographic record
Abstract
Rapid industrialization of North American cities during the nineteenth century was associated with periodic innovations in transportation and massive increases in traffic, which, in turn, caused perennial problems of congestion in ill-adapted urban cores. During the latter half of the nineteenth century, the municipal government of Montreal expropriated and destroyed thousands of properties to widen dozens of existing streets. This paper argues that the key to these acts of "creative destruction" was the removal of barriers to circulation through a periodic redimensioning of the "urban vascular system, " and hence, a speed up in the rate of urban growth. A detailed investigation of the planning and execution of major street widening projects between 1862 and 1900 reveals how the built environment of Montreal was periodically destroyed and recreated by a local growth coalition committed to increasing aggregate rents, property values, and municipal revenues, through the intensification of land use. Examination of a sample of properties before and after street widenings suggests that redevelopment was most intense during economic boom periods and in central areas, when and where competition for space was most extreme, and there existed the greatest pressure to remodel the built landscape to fit the needs of a changed economic 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 | 0.002 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".