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Record W2774651318 · doi:10.7202/1042294ar

Urban Elites, Energy, and Smoke Policy in Montreal during the Interwar Period

2017· article· en· W2774651318 on OpenAlexvenueaboutno aff
Owen Temby, Joshua MacFadyen

Bibliographic record

VenueUrban History Review · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsSmokeGovernment (linguistics)BusinessUrban transitEconomyPublic administrationPolitical scienceEconomicsEngineeringPublic transportLawWaste management

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.383
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.020
GPT teacher head0.251
Teacher spread0.231 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations1
Published2017
Admission routes2
Has abstractyes

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