Bibliographic record
Abstract
Livable communities are a key focus for urban planners around the world, and transportation is arguably one of the most important factors that contribute to neighbourhood livability. The term ‘livability’ is subjective, and while there have been attempts to examine how walking and public transit may improve the livability of neighbourhoods and transit corridors, very few studies have focused on the role of bicycle infrastructure. My research begins to close this gap by 1) proposing a conceptualization of neighbourhood livability, as it relates to bicycle infrastructure, and 2) exploring the association between cycling facilities, specifically painted bicycle lanes and on-street cycle tracks, and perceived neighbourhood livability. Subjective perceptions of livability were captured through an online survey in 17 urban and suburban neighbourhoods within the Greater Toronto and Hamilton Area with a recently built on-street cycling facility (case study sites) and six without (case-control sites). Factor analysis was conducted to create three meaningful factors representing perceptions of various aspects of neighbourhood livability and weighted multivariate linear regression models were estimated with each factor as a dependent variable. Results indicate an association between newly built cycling facilities and improved perception of neighbourhood livability, and support the policy emphasis on active transportation, particularly bicycle infrastructure, in creating better communities.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".