Riders, Not Drivers of Change: how parking regulations can shape a city’s future.
Bibliographic record
Abstract
Many major cities around the world today have been designed for cars and not for people. In Canada and the US, most built infrastructure is devoted solely to cars – roads, highways and parking, and has even led us to conceptualize the city as a car-based space. Cars do serve a role in our lives, and for some, are essential in meeting everyday needs. Designing our living spaces excessively around them, however, has resulted not just in substantial societal costs and adversity, but has also altered our perception of living spaces themselves. Continuing to devote these spaces to car infrastructure perpetuates our dependence on cars. Building parking spaces in particular, uses substantial land and resources while failing to offer viable societal returns on costs. \nRealizing the costs and consequences of building excess parking in Toronto, the city municipal body has recently revised its parking requirements policy, abolishing parking minimum requirements, and replacing them with parking maximums. This amendment is driven by the city’s vision for a more liveable, sustainable, transit-oriented city, that is less dependent on cars, as stated in its official plan. This research study, in the form of a Major Portfolio, explores in depth the reasons behind the implementation of this policy revision, and its implications for residents and commuters, as well as what it means for the identity of the city itself. The study investigates these questions through the perspectives and responses of stakeholders, experts, developers and planners, and the community, which are collected through interviews, and adopts a behavioral lens in its analyses of the issue. \nMy research indicates that Toronto’s parking policy revision is a step in the right direction, but a small step, and one that is unlikely to lower housing costs to home buyers/renters. It can help developers and other stakeholders through cost savings due to less money spent on constructing unnecessary parking stalls, and it will help reduce the proportion of unused parking spaces for the future. However, it needs to be complimented with more radical changes in order to reduce car dependence, to encourage people to shift to healthier and more sustainable methods of transportation, and make housing more accessible to people.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".