MétaCan
Menu
Back to cohort
Record W4225133347 · doi:10.1177/23996544221092920

De-politicising and re-politicising transport infrastructure futures

2022· article· en· W4225133347 on OpenAlexaboutno aff
Crystal Legacy

Bibliographic record

VenueEnvironment and Planning C Politics and Space · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Planning and Governance
Canadian institutionsnot available
FundersAustralian Research Council
KeywordsFutures contractPoliticsWork (physics)Transformational leadershipPolitical scienceSociologyPolitical economyPublic administrationPublic relationsEconomicsLawEngineering

Abstract

fetched live from OpenAlex

The planning for future transport and its infrastructure is deeply political. Yet, how we understand re-politicisation, and what those efforts tell us about what is political in the planning for future cities, remains under explored. One lens through which to explore these acts is to consider the role of urban coalitions in drawing attention to the dominant politics of planning and setting the ground for the re-politicisation of transport infrastructure futures. Drawing on the work of post-foundational scholars Mouffe and Rancière, this paper examines the interplay between de-politicisation and re-politicisation and how two urban coalitions negotiated this landscape in the Greater Toronto and Hamilton Area during a sustained period of contestation surrounding the proposal of new transport infrastructure. Through this analysis, this paper draws on in-depth interviews with coalition members, transport planners, politicians and engaged citizens to illustrate how these urban coalitions produced a ‘collective will’ and a struggle towards a ‘consensus cure’ in their re-politicising actions. This paper reveals how coalition-led re-politicisation establishes the grounds for the politics to shift on contested future transport proposals and offers insight into the incremental and oftentimes incomplete ways re-politicisation nurtures transformational change.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.536
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 studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.011
GPT teacher head0.242
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 designObservational
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
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueEnvironment and Planning C Politics and SpaceSame topicUrban Planning and GovernanceFrench-language works237,207