Sustainable Neighborhoods and Housing Affordability in Canada: Is housing affordable in neighborhood with more favorable active living environments?
Bibliographic record
Abstract
Sustainable neighborhoods are often praised as being model areas, as walking and other modes of active transportation found in them are more accessible. Active living environments are a dimension of sustainable neighborhoods, being areas which promote active living - i.e. a way of life that integrates physical activity into daily routines (Sallis et al., 2005, p.93). Little attention is given to whether affordable housing is found in sustainable neighborhoods. My research explores this question by first examining the variation in housing affordability by neighborhood active living potential in all of Canada, as well as in ten Canadian Census Metropolitan Areas (CMAs). I use statistical methods and data from the 2016 Canadian Census and the Canadian Active Living Environment database. I then turn to field observations in Montreal in order to better understand this relationship on the ground. Findings suggest that neighborhoods more favorable to active living have higher proportions of housing that are unaffordable, but that this relationship varies in different CMAs. Results from field observations suggest that there are micro-scaled, local specificities which may inform why certain environments favorable to active living are affordable and others are not. I end with a few suggestions to inform policy and indicate how to build on my research.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".