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Record W4252713914 · doi:10.32920/ryerson.14648220.v1

Bikes and Belonging: A Photographic Exploration of the Bike Host Program in Toronto

2021· preprint· en· W4252713914 on OpenAlexaboutno aff
Yvonne Verlinden

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsnot available
Fundersnot available
KeywordsTRIPS architectureDestinationsImmigrationRefugeeNeighbourhood (mathematics)Host (biology)GeographySociologyAdvertisingPsychologyVisual artsEngineeringTransport engineeringTourismBusinessArtArchaeology

Abstract

fetched live from OpenAlex

This major research project uses photography to explore questions of mobility, place-learning and belonging with newcomers participating in the Bike Host program in Toronto. Created by CultureLink Settlement Services in 2011, Bike Host loans bicycles out to immigrants and refugees and matches them with a cycling mentor. Through small group rides and large events, the participants have the opportunity to explore Toronto, gain confidence riding, make social connections, practice English and engage in volunteerism. For this project, a dozen participants also took pictures of how they were using their bicycles and shared their photos in small group, semi-structured discussions, which were recorded and analyzed. Four themes emerged: freedom, comfort and knowledge, discovery and belonging. The photographers found that compared to walking, they could travel further more quickly and with less effort, which prompted them to make more trips within their communities. The photographers also appreciated that, unlike with transit, they could leave whenever they wanted and take whichever route they wanted. This new mobility led to discovery, in both guided group rides to iconic Toronto destinations and in neighbourhood meanderings, undertaken independently along local streets and trails. Through this process, they filled in the gaps in their local cognitive maps. Increased familiarity led to an increased sense of belonging, as places that were once unfamiliar began to feel more like home.

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)0.002
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 score0.298
Threshold uncertainty score0.929

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.510
GPT teacher head0.621
Teacher spread0.111 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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