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Record W3192692839 · doi:10.3390/su13168680

Creating Sustainable Cities through Cycling Infrastructure? Learning from Insurgent Mobilities

2021· article· en· W3192692839 on OpenAlexafffund
Aryana Soliz

Bibliographic record

VenueSustainability · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicWater Governance and Infrastructure
Canadian institutionsConcordia University
FundersConcordia UniversitySocial Sciences and Humanities Research Council of CanadaGovernment of Canada
KeywordsMobilitiesCONTESTSustainabilityGovernment (linguistics)CitizenshipWork (physics)Sustainable transportVariety (cybernetics)Political scienceNegotiationPublic relationsSociologyPublic administrationPoliticsEngineeringSocial science

Abstract

fetched live from OpenAlex

As policy makers grapple with rapid motorization processes, cycling facilities are gaining new urgency, offering non-polluting and affordable alternatives to automobility. At the same time, urban sustainability paradigms tend to focus on purely technical solutions to transportation challenges, leaving questions of history and social power aside. Drawing from ethnographic fieldwork in Aguascalientes Mexico, this article contributes to the transportation and mobility justice literature by focusing on the work of social movements in confronting a variety of challenges in the provision of active-transportation services. First, this research explores how social movements express and negotiate transportation-justice concerns to government and planning authorities. Next, I build on the concept of insurgent citizenship to highlight the processes through which residents contest ongoing injustices and formulate alternatives for building inclusive cities. From the creation of makeshift cycling lanes in underserved urban areas to the search for socially just alternative to policing, social movements are forging new pathways to re-envision sustainable transportation systems. These insurgent forms of citymaking—understood here as insurgent mobilities—underscore the creative role of citizens in producing the city as well as the enormous amount of care work involved in these processes.

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)0.001
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.499
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.276
Teacher spread0.265 · 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 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

Citations5
Published2021
Admission routes2
Has abstractyes

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