Sweeping the city: infrastructure, informality, and the politics of maintenance
Bibliographic record
Abstract
Uneven development in many North American cities has given rise to an increasing number of homeless encampments as residents seek shelter, sanitation, and other basic needs outside of formally recognized networks. In gentrifying cities, these informal infrastructures are also subject to the recurring violence of sweeps, wherein states remove, seize, or destroy life-sustaining necessities to decrease their visibility and designate space for other uses. The sweep is both a strategy of governance and a viscerally felt phenomenon in which infrastructural networks become terrains of contestation. In this article, we examine the cultural politics of sweeps through the analytic of maintenance. Drawing on examples from Toronto, Ontario, Canada and San Francisco, California, United States, we argue that sweeps are mechanisms of policing that often operate through the apparently benign work of routine maintenance, which in turn iteratively organizes belonging and exclusion in cities. These dynamics have thereby become important sites for infrastructural struggle. With our analysis, we join scholars in the study of infrastructure taking a critical stance toward processes of maintenance and repair, asking questions about what is being maintained, for whom, and toward what end.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.050 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".