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Record W4388832079 · doi:10.1080/21650020.2023.2277804

Bicycling for mutual aid: centering racialized and 2SLGBTQ+ cyclists in Toronto

2023· article· en· W4388832079 on OpenAlexafffundabout
Jessica Nachman, Lyndsay Hayhurst, Rachel Wang

Bibliographic record

VenueUrban Planning and Transport Research · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsMarch of Dimes CanadaYork University
FundersSocial Sciences and Humanities Research CouncilMitacsCanada Foundation for Innovation
KeywordsMutual aidTimelinePublic relationsSolidaritySociologyThrivingContext (archaeology)Political sciencePoliticsGeographySocial science

Abstract

fetched live from OpenAlex

ABSTRACTWithin the context of a (post-)COVID-19 pandemic world, there is an urgent need to critically explore how bicycle-related activities may contribute to an environmentally sustainable and equitable world for vulnerable populations. In recent years, mutual aid projects have surged globally, with scholars pointing to the COVID-19 pandemic as a key driver of communities being forced to respond to the unfolding social and environmental crises, alongside state abandonment. In this paper, we discuss how cycling has been taken up by communities disproportionately harmed by colonial systems. Using a decolonial feminist participatory action research approach, the authors collaborated with The Bike Brigade, a non-profit bicycle delivery organization that partners with mutual aid organizations. Using arts-based methods and semi-structured interviews, we draw on the perspectives of 2SLGBTQ+ and racialized cyclists who volunteer with The Bike Brigade. A key theme of the research was the unique way in which research colleagues used bicycles to participate in community care by embodying mutual aid values: community thriving, resource reallocation and solidarity. Thus, this paper puts forth mutual aid as a potential framework for understanding radical mobility practices to foster community care.

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)0.003
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.123
GPT teacher head0.468
Teacher spread0.344 · 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 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

Citations4
Published2023
Admission routes3
Has abstractyes

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