Why We Still Own Cars: An Ethnographic Case Study of Car Ownership and Use in Ottawa
Bibliographic record
Abstract
This dissertation comprises the results of a two year-long ethnographic study of car ownership and use in Ottawa, Canada.Based on interviews and observational data from thirty-two participants, this study aims present a profile of Ottawa residents' relationships to cars by providing an interpretation of cultural meanings and practices surrounding car ownership in a primarily urban setting.Building on literature from a variety of sources within anthropology and sociology, the major findings of this dissertation are broken into five interrelated chapters covering the diverse meanings and practices which construct car ownership as a necessary part of everyday life in Ottawa: 1) having a car is a response to the affordances of the built environment of Ottawa, 2) it means experiencing a desirable body; 3) it means having the human capital to realize complete flexibility and independence in one's economic and social pursuits; 4) it means being able to configure social relationships with transcendent cultural values and through the deployment of space and distance; 5) it means articulating autonomous politics and making sense of the interconnections between competing and complementary ideologies (such as freedom, independence, capitalism, family, work, able-bodiedness, modernity, and sustainability) which develop and change through everyday engagements with cars.In the final chapter of this dissertation, several questions are raised to prompt designers, social scientists, city planners, and citizens to think about how they might contribute to a popular shift towards a new kind of relationship to cars: one which is characterized by intentionality and choice, rather than by feelings of necessity.This dissertation would not have been possible without the kind help and support of a number of people.I would like to extend my gratitude to them.First, I would like to thank my supervisor, Dr. Danielle DiNovelli-Lang, for integral support and guidance.This project would not have been finished, let alone successfully so, without her wisdom and friendship.I would also like to thank the other members of
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.025 | 0.013 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".