Book Review: Everyday Law on the Street: City Governance in an Age of Diversity, by Mariana Valverde
Bibliographic record
Abstract
MARIANA VALVERDE'S RECENTLY pubLISHED bOOk on the intersections between municipal decision making and the representation of diverse communities in theCity of Toronto is a spellbinding and innovative read for scholars and students engaged in socio-legal research.It sheds remarkable light on decision making within Canada's largest city-not in committee rooms, but by unseen political and staff practices and quasi-judicial bodies-and the corresponding absence of meaningful social inclusion and political participation of diverse groups.Valverde offers compelling evidence that the City of Toronto must revisit and reject Jane Jacobs' "village life" 3 ideal of community involvement in favour of deeper governance models that recognize the representative needs of complex cities.This book fills an important gap in municipal scholarship by examining how street-level decisions are made in the urban context and the implications for those who are disadvantaged by such processes.The book begs further examination of ways in which existing institutions of governance can be reformed and the City of Toronto Act 4 can be better exercised to address the significant problems that Valverde identifies.Everyday Law on the Street is divided into nine succinct chapters.The first two chapters introduce the book's scope, clarifying Valverde's relationship to theories of community decision making advocated by Jane Jacobs and others, and identifying Valverde's definition of diversity.The remaining chapters analyze specific case studies related to maintenance and permits, business regulation, and zoning to examine critically the implications of city decision making for diverse populations.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.019 | 0.009 |
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".