Liberty and Security in an Austere City: Security Politics and Urban Restructuring in Post-Bankruptcy Detroit
Bibliographic record
Abstract
Detroit’s experience with emergency management and municipal bankruptcy has been the subject of intense public and academic scrutiny. Yet, few have explored how this moment of intense urban restructuring helped to reinforce security as a vital pillar of the city’s revitalization strategy. In response to this gap in scholarship, this dissertation explores highly experimental, post-bankruptcy securitization efforts with an empirical focus on the origins and growth of the city’s ‘real-time’ crime fighting initiatives and a city-wide CCTV initiative called Project Green Light. Formally introduced in 2016 as a ‘public-private-community’ partnership, Project Green Light involves a voluntary agreement whereby participating businesses agree to fund the installation and maintenance of cameras on their premises that can be actively monitored in real-time by the Detroit Police Department. In exchange, Project Green Light partners are promised prioritized police response and enhanced police presence. Conceiving of Project Green Light as a form of speculative security, this dissertation examines how the program has expanded despite conclusive evidence of its efficacy, numerous controversies surrounding its objectives and rollout, and resistance from community residents and grassroots organizers. The study draws from a large selection of documents as well as interviews with Project Green Light participants and neighborhood residents in order to explore perceptions and experiences of the program. In doing so, this dissertation unravels the contested politics around speculative visions of security and surveillance that have been intimately bound up in efforts to remake the City of Detroit.
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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 | 0.002 | 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 | 0.018 | 0.021 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".