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Record W2564003128 · doi:10.3138/jcs.2016.50.1.5

Breaking New Ground: Montreal—Mirabel International Airport, Mass Aeromobility, and Megaproject Development in 1960s and 1970s Canada

2016· article· en· W2564003128 on OpenAlexvenueaboutno aff
Bret Edwards

Bibliographic record

VenueJournal of Canadian Studies · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicUrbanization and City Planning
Canadian institutionsnot available
Fundersnot available
KeywordsMegaprojectIdeologyUrban sprawlTechnocracySociologyGovernment (linguistics)Political sciencePublic administrationUrban planningMedia studiesLawManagementEngineeringPoliticsCivil engineeringEconomics

Abstract

fetched live from OpenAlex

In October 1975, Montreal—Mirabel International Airport opened to the travelling public. This article examines why the federal government and its partners embraced megaproject ideology to build Mirabel, how these multiple institutions formulated and negotiated their airport vision, and its subsequent impact on the local land and people. It argues that politicians and planners broke new ground with Mirabel, fashioning it as a site of and for mass aeromobility that would be much more than a new airport. Influenced by postwar pro-growth discourse, as well as developments at the city and provincial level, they settled on an unprecedentedly large airport that could expand well into the future jet age. To make this vision a reality, however, Mirabel’s boosters overlooked or deliberately disregarded the socio-environmental effects of an airport megaproject, choosing to impose a distinct aeromobile landscape that altered the character and identity of the local area. In the process, they endorsed a technocratic idea of development that accelerated jet age sprawl as modernized airports like Mirabel became more spatially dominant and disruptive within natural and built environments.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.197
Threshold uncertainty score0.932

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0210.014
Scholarly communication0.0080.002
Open science0.0010.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0070.000

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 head0.043
GPT teacher head0.284
Teacher spread0.241 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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

Citations6
Published2016
Admission routes2
Has abstractyes

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