Bibliographic record
Abstract
Complete Streets have been lauded for their environmental and social benefits, but quantifying those benefits can be challenging.This thesis uses a difference-in-differences method to evaluate how Complete Streets in Toronto, Ontario impacted residential property values.Using a dataset of nearly all residential properties sold in the city, I find that residential property values near a Complete Street conversion increase by, on average, 10.5%.However, the magnitude and significance of that impact vary around each street.Results contrast with other on-and off-street bike lane installations, which did not significantly impact house prices, but questions remain about the exact mechanism of a Complete Street's valorizing effects.Understanding how and when Complete Streets influence property values can be a helpful policy input for municipalities interested in capturing value from cycling infrastructure investments.However, further research is needed to understand how these effects might impact the equitable distribution of housing.Many years ago, Dr. Abel Brodeur reviewed the very first model I used to test the property value response from Complete Streets.At the time it was only a proof-ofconcept with a few hundred houses worth of data, but he helped me focus the model and tested some of my early intuitions.Though this research has evolved substantially since his original input, I would be remiss without thanking him for starting me on this path in the first place.Since then, I have been fortunate to have received helpful instruction from Dr. Pablo Mendez and Dr. Matt Webb.Your guidance and suggestions, and the interdisciplinary combination of your insights, in particular, have been invaluable.The experience of writing this thesis with you both has been a pleasure, and I look forward to our future work together.I also want to thank the faculty and administration in the Department of Geography and Environmental Studies.Their dedication and support of the DGES students are unwavering and always appreciated.Thank you to Dr. Ardyn Nordstrom for constantly cheering me on
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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 | 0.002 | 0.012 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".