Planned Inefficiency: Defining and Defending the Public Realm in the Age of Autonomous Vehicles
Bibliographic record
Abstract
The urban public realm is an increasingly contested space.As disruptive technologies continue to enter North American cities, they exert outsized impacts on urban environments, governance, and social constructs.Meanwhile, the urban policies which dictate the implementation of these technologies are frequently designed in service of their monetization schemes, rather than citizen welfare.Architecture is a discipline uniquely capable of making complex information accessible to the public.Architecture can spatialize, translate and interpret the complexities and challenges which disruptive technologies pose to cities, empowering their citizens.Responding to the case study of autonomous vehicle technologies in Toronto, this thesis contributes to an expanded definition of architectural practice, utilizing architectural thinking and working methods to test novel approaches for understanding and protecting democratic urban governance in the 21st century.IV IV. Acknowledgements To my advisor, Zach Colbert -thank you for your support and encouragement.It enabled me to chase concepts further, dig deeper and ultimately develop my thinking in ways I never expected.Your gentle guidance has been vital in keeping me on track and confident in developing my work to its fullest potential.It has been a pleasure to be your student -both times.I am grateful to my lifelong friends at home, and those whom I made along the way for helping me smile, laugh, and enjoy life.The experiences and bonds we have built are invaluable me and motivate me to grow.A special thanks to Cole Peters whose conversation and reading suggestions have been invaluable to my academic development.Finally, I would like to thank my family.My parents Linda and Ed, without the many varieties of your support I could not have completed this education, your dedication
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.043 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".