Detroit’s Project Green Light: an experiment in police surveillance as economic development strategy
Bibliographic record
Abstract
Literature on territorial stigma has clearly outlined the ways that stigma as a discursive strategy has been enacted as a rationale for policy intervention to control populations viewed as disorderly or antithetical to capitalist development goals. However, these studies have been largely based on cities with growing economies, and have not adequately applied the framing of territorial stigma to understand how declining cities may operationalize stigma in new and consequential ways. This paper explores Detroit’s Project Green Light, an initiative from the Detroit Police Department that allows for small businesses to directly stream surveillance footage to the police command center at their own expense in exchange for shorter emergency response times. The case of Project Green Light exemplifies how territorial stigma is leveraged against the Black residents of a declining city to rationalize new, experimental uses of policing and privatization towards economic development goals in disinvested areas of the city. This case not only shows the ways that environments of severe decline may enact racial stigma uniquely in their economic development goals, but also illustrates how policies, born from experimentation, are important to watch due to their mobilization elsewhere.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".