Bibliographic record
Abstract
Intensification is the key planning policy and growth management approach in Ontario, as well as across most of North America. Under this larger context, the Region of Waterloo, Ontario is building a Light Rail Transit (LRT) to provide alternative public transit option and help reshape land development, with the goal of increasing the development density in core areas, increasing mixed-use development, and curbing urban sprawl. To better understand how the upcoming LRT will influence housing choices and development patterns, this thesis explores households’ location choice decision and perceptions of LRT from a renters’ perspective. From June to November 2016, a random sample of 2912 households renting in Kitchener-Waterloo were invited to participate in a survey on residential location choice, renting behaviours and perceptions towards the upcoming LRT, after which a total of 290 surveys were analyzed. After a descriptive analysis of the survey results, a hedonic model was also developed to investigate the relationship between rental housing prices and corresponding household, residential, neighbourhood and behaviour characteristics. Unlike other aggregate level models, this hedonic model is implemented using individual level household information collected through the customized survey. The structure of rental housing demand is unveiled regarding different resident groups, as well their perceptions and preferences towards different residential and neighbourhood characteristics. Findings from this study could also be applied to inform housing polices, regarding housing development and housing affordability.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".