Seeing Like a City: The Dialectic of Modern and Premodern Ways of Seeing in Urban Governance
Bibliographic record
Abstract
Studies of urban governance, as well as the overlapping literature on law and space, have been heavily influenced by critical analyses of how spatial techniques helped constitute modern disciplinary powers and knowledges. The rise of land-use control and land-use planning seem at first sight to be perfect examples of the disciplining of populations through space by the kind of governmental gaze dubbed by Scott (1998) as “seeing like a state.” But a detailed genealogical study that puts the emergence of the notion of “land use” in the broader context of urban governance technologies reveals that modernist techniques of land use planning, such as North American zoning, are more flexible, contradictory, and fragile than critical urbanists assume. Legal tools of premodern origin that target nonquantifiable offensiveness and thus construct an embodied and relational form of urban subjectivity keep reappearing in the present day. When cities attempt to govern conflicts about the use of space through objective rules, these rules often undermine themselves in a dialectical process that results in the return to older notions of offensiveness. This article argues that the dialectical process by which modernist “seeing like a state” techniques give way to older ways of seeing (e.g., the logic of nuisance) plays a central role in the epistemologically hybrid approach to governing space that is here called “seeing like a city.”
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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.036 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".