Bikes and Belonging: A Photographic Exploration of the Bike Host Program in Toronto
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".