Expertise, Local Knowledge, and the Construction of the Automobile as an Environmental Risk in Montreal, 1960s–70s
Bibliographic record
Abstract
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.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.017 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".