Crime, Street Vendors and the Historical Downtown in Post-Giuliani Mexico City
Bibliographic record
Abstract
This article endeavors to go deeply into the recent transformations that have taken place in the regulation of street-level economic and business activities in Mexico City. It draws upon data collected during the course of a three-year research project carried out from 2007 to 2009, a specific timeframe when the urban authority deployed different legal and repressive strategies in order to ‘clean-up’ the streets of the city’s downtown areas, in keeping with the Giuliani Group’s advice. This paper intends to clarify two different dynamics: (a) how the urban authorities went about applying Giuliani’s advice to clean up the streets, and (b) the consequences these initiatives may have on the historical downtown core. My principal task is to offer a tentative insight into whether the incorporation of Giuliani’s repressive approach to urban planning has affected a specific urban space where, for decades, street-level economic, business and trade activities have been intimately interrelated with the creation of a city’s street culture. Research findings suggest that in those countries where street economic activities constitute a die-hard method of eking out one’s sustenance, the relation between order and crime may be more porous and indeterminate than is recognized and acknowledged by the majority of sociolegal studies that have, over the course of time, developed around this topic.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".