Authorizing the Production of Urban Moral Order: Appellate Courts and Their Knowledge Games
Bibliographic record
Abstract
Using some appellate courts' reviews of projects to maintain moral order in the city as the main source of data, this article shows that an analysis of legal knowledge production that (1) is dynamic and (2) refuses to treat people and texts as totally different entities, one studied by social scientists and the other studied by lawyers, can tell us much about such familiar but seldom theorized legal maneuvers as judicial review and constitutional challenges. Choosing to analyze the dynamics of knowledge processes is inspired by Actor Network Theory (ANT), Bruno Latour's work in particular. This methodological choice is particularly appropriate because judicial review tends to avoid making judgments about the content of impugned laws or ordinances, focusing instead, as Latour does, on form and process. But insofar as legal processes in general privilege form and process to a greater or lesser degree, a more general argument is made about the appropriateness of using tools from ANT to study legal and quasi-legal knowledge networks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".