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Record W3036073863 · doi:10.3138/chr-2018-0102

Expertise, Local Knowledge, and the Construction of the Automobile as an Environmental Risk in Montreal, 1960s–70s

2020· article· en· W3036073863 on OpenAlexvenueaboutno aff
Valérie Poirier

Bibliographic record

VenueCanadian Historical Review · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsProsperitySymbol (formal)RationalitySociologyCitizen journalismModernityDemocracyEconomic growthPoliticsPolitical scienceEconomicsLaw

Abstract

fetched live from OpenAlex

Since its introduction in North America, the automobile has reshaped the economy, transformed the way people travelled around, changed urban landscapes, and been a powerful symbol of freedom, prosperity, and progress. Yet, in the 1960s and 1970s, this symbolism was seriously challenged. This article explores the process by which the car came to be perceived as an environmental risk in Montreal and the responses to manage this risk. It identifies two groups of social actors that were instrumental in shaping perceptions of the automobile as a risk – namely, municipal experts and an ensemble of five local environmental groups. Both actors believed that the environmental risks of the car were real dangers arising from modernity. While the municipal experts narrowly focused on the health risks of automobile pollution, which led to technical and technological solutions to reduce this risk, the environmental groups put forward a much broader critique of the place of the car in our culture, cognizant of the ways it magnified class, gender, and other inequalities. While they tried to transform the ways that we moved through cities, they also attempted to improve participatory democracy mechanisms to ensure the citizen’s right to participate in decisions that affected their environment. Hence, this article argues that the two groups’ vision of this risk and their responses were historically specific, in that each of them was anchored in, and shaped by, the groups’ knowledge, set of values, rationality, objectives, social position, and relation to power.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.778
Threshold uncertainty score0.997

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 science0.0000.000
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 head0.010
GPT teacher head0.221
Teacher spread0.210 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations3
Published2020
Admission routes2
Has abstractyes

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