Urban Elites, Energy, and Smoke Policy in Montreal during the Interwar Period
Bibliographic record
Abstract
During the late 1920s and early 1930s, Montreal’s air was blackened by smoke from coal-burning homes, factories, and the locomotives and lake freighters connecting its growing economy to the rest of Canada. Lacking regulatory tools suited to the task of abating this nuisance, the municipal government passed the country’s first modern smoke bylaw, consisting of an objective emissions standard, a smoke control bureau, and requirements for the installation and utilization of technology to lessen emissions. In providing an account of the process through which Montreal’s smoke nuisance was addressed, this article describes the role of the city’s most influential local growth coalition, the Montreal Board of Trade, in introducing the issue on the city’s policy agenda, participating in the formulation of a policy response, and monitoring the implementation of the resulting bylaw. The Board of Trade sought a resolution to the problem because it damaged the city’s reputation and business climate. Consistent with other documented examples of smoke abatement in large urban areas, the response promoted by this elite growth coalition consisted largely of technology-based measures that managed the problem while eschewing recourse to measures that would dampen economic activity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".