‘Shadowing the state’: Subaltern surveillance and the rhythms of everyday resistance
Bibliographic record
Abstract
This paper challenges and unsettles dominant discourses on spatial control by conceptualizing subaltern surveillance as an everyday counter-practice through which street traders negotiate access to contested spaces. While literature is loaded with state-centric perspectives on surveillance – less attention has been paid to how street traders flip the surveillance gaze. How do street traders engage in subaltern surveillance to negotiate access to contested urban spaces, and what do these practices reveal about power relations in cities? Building from ethnographic inquiry in Harare, Zimbabwe, I demonstrate how street traders use sophisticated spatial and temporal knowledge of municipal enforcement rhythms and deploy this locally embedded everyday wisdom to undermine dominant surveillance and spatial control. The paper situates subaltern surveillance within broader discussions on urban informality, everyday resistance, and the right to the city, arguing that street traders’ acts of watching, predicting, and adapting are not merely survival tactics but also political maneuvers that challenge repressive domination. This framing challenges the dominant narratives that construct surveillance as a predominantly top-down practice. By so doing, the study invites new dialogues on governance and lays the groundwork for a critical analysis of the dialectical relationship between subaltern surveillance and dominant urban governance logics.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.042 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".