On People In Changing Neighborhoods. Gentrification and Social Mix: Boundaries and Resistance. A comparative ethnography of two historic neighborhoods in Milan (Italy) and Brooklyn (New York, USA)
Bibliographic record
Abstract
This paper focuses on the study of urban transformations in two historic, inner city neighborhoods: Paolo Sarpi Street, the so-called "Milan Chinatown" in Italy, and Park Slope, whose history reflects the waves of immigrants who helped create Brooklyn's character in New York City.These cases embody two unique urban environments undergoing several processes of gentrification since the 1970s.The Milan Chinatown is represented by a handful of streets, the global flow of Chinese goods and the daily routines of elderly people and families.The complexity of the "Sarpi Question" is precisely determined by the discussion of social dimensions, space and ethno-racial, economic and political, all at once. Park Slope is distinguished for being the largest landmark district in Brooklyn, and enjoys quiet, tree-lined streets with wide architectural variety.Progressive yuppies and establishment lesbians have long ruled the classy section of the Slope, in particular the named streets between 7th Avenue and Prospect Park.These days the action is happening all along 5th Avenue and in the so-called "South End" of the Slope.Given this background, the discussion begins with a comparative analysis, on the one hand, of the deepening of the causes which led to the break of an apparent balance in the practices of local cohabitation of the Milan's Chinese District.On the other hand, the New York case study aims to address the issue of neighborhood changes and renewal through a specific interpretation key: a changing neighborhood as a place of symbolic elaboration of socio-cultural boundaries.Through a wide ethnographic empirical demonstration 2 , this contribution mobilizes a set of ideas concerning the academic and political debates surrounding the gentrification and social mix of the contemporary city.
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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 | 0.002 |
| Science and technology studies | 0.014 | 0.014 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| 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".