The Ethic of Diversity: Local Law and the Negotiation of Urban Norms
Bibliographic record
Abstract
Toronto prides itself both on being diverse and on celebrating rather than merely tolerating diversity. Urban diversity has been studied by demographers, sociologists, and planners, but sociolegal analyses of the negotiation of diversity are scarce. The study described here has three elements: a study of the Toronto Licensing Tribunal, a challenge to the property standards by‐law, and a campaign to reform the rules governing street food. The key substantive finding of the research is that municipal legal processes, in a city that takes pride in its diversity, still work to effect and naturalize distinctly ethnocentric norms. The content of (some) norms is subject to revision but the normative power of law as such remains unchallenged. Methodologically, the article, inspired by Bruno Latour and Actor Network Theory, shows the usefulness of treating local legal processes as a series of networks in which nonhuman objects (such as weeds, courtroom Bibles, and hot dogs) can sometimes be protagonists of legal dramas rather than mere objects.
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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.053 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| 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".