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Record W4396218889 · doi:10.1145/3637373

Moving Towards Mobility Justice: Challenges and Considerations for Supporting Advocacy

2024· article· en· W4396218889 on OpenAlexaffabout
Taneea S Agrawaal, Samar Sabie, Robert Soden

Bibliographic record

VenueProceedings of the ACM on Human-Computer Interaction · 2024
Typearticle
Languageen
FieldComputer Science
TopicInnovative Human-Technology Interaction
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsEconomic JusticePublic transportPoliticsSocial justiceUrbanizationTransportation planningPublic relationsSociologyPolitical scienceTransport engineeringEconomic growthEngineeringCriminologyLawEconomics

Abstract

fetched live from OpenAlex

In response to climate change and continued urbanization, urban transportation systems around the world are undergoing transitions to promote lower-emission vehicles, public transit, biking and walking. However, mobility is a complex issue that raises important questions of social justice as a result of its connections to numerous aspects of everyday life and the broader social and political contexts. Drawing on interviews with transportation advocates across Canada, we identify four ways in which the design and use of existing mobility tools and technologies perpetuate mobility injustices, and deepen the divide between urban planners and the public. Looking across these arguments, we note path-dependence in transportation knowledge infrastructures as a common barrier to mobility justice advocacy that can be difficult to recognize or overcome. Finally, we consider tactics that research in HCI and CSCW might pursue as part of efforts to unsettle path-dependence and reorient transportation planning towards mobility justice.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.197
Threshold uncertainty score0.753

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0010.001
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 head0.105
GPT teacher head0.379
Teacher spread0.274 · 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 designTheoretical or conceptual
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

Citations1
Published2024
Admission routes2
Has abstractyes

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