Bibliographic record
Abstract
How local, personal, and materially grounded understandings about belonging, ownership, and agency intersect with law to shape the city. In Owning the Street, Amelia Thorpe examines everyday experiences of and feelings about property and belonging in contemporary cities. She grounds her account in an empirical study of PARK(ing) Day, an annual event that reclaims street space from cars. A highly recognizable example of DIY urbanism, PARK(ing) Day has attracted considerable media attention, but not close scholarly examination. Focusing on the event's trajectories in San Francisco, Sydney, and Montréal, Thorpe addresses this gap, making use of extensive fieldwork to explore these tiny, temporary, and yet often transformative urban interventions. PARK(ing) Day is based on a creative interpretation of the property producible by paying a parking meter. Paying a meter, the event's organizers explained, amounts to taking out a lease on the space; while most “lessees” use that property to store a car, the space could be put to other uses—engaging politics (a free health clinic for migrant workers, a same sex wedding, a protest against fossil fuels) and play (a dance floor, giant Jenga, a pocket park). Through this novel rereading of everyday regulation, PARK(ing) Day provides an example of the connection between belief and action—a connection at the heart of Thorpe's argument. Thorpe examines ways in which local, personal, and materially grounded understandings about belonging, ownership, and agency intersect with law to shape the city. Her analysis offers insights into the ways in which citizens can shape the governance of urban space, particularly in contested environments. The book's foreword is by Davina Cooper, Research Professor in Law at King's College London.
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 | 0.001 | 0.001 |
| 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 | 0.010 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.037 | 0.005 |
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".