Defending the innovation district: Violence, urban entrepreneurialism, and the privatization of public security in Monterrey, Mexico
Bibliographic record
Abstract
Over the past four decades, urban scholars have paid increased attention to how private sectors pursue capital gain in and through the city, including through carving out innovation districts. While the privatization of public space is well-established in this literature, less is known about how entrepreneurial districts can further the privatization of public security. Drawing on and extending this line of work, this article examines how a private sector can leverage the local state, an anchor institution (a university), and district residents to create what we call a defended entrepreneurial district— an entrepreneurial district receiving priority public–private policing from above and heightened neighborhood vigilance from below to secure a territory for capital gain in a high-violence context. Our case study is the Distrito Tec, a private university-led innovation district launched in 2014 in Monterrey, Mexico in the aftermath of a major metropolitan security crisis. While the university removed walls from its block perimeter, we argue that it simultaneously reinstated new forms of socio-spatial enclosure at a district level. New public–private policing initiatives deepened surveillance of the 24 blocks in its vicinity favoring the concentration of capital within while prompting new divisions among unwilling residents and the exclusion of unwanted populations. Methodologically, we combine qualitative data on socio-spatial responses to violence collected 6 years apart—during the height and aftermath of this public security crisis. Our comparative analysis contributes to conversations on ethnographic revisits, while advancing research on the privatization of public security in entrepreneurial districts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".