1The Transformation of Ethnic Neighborhoods into Places of Leisure and Consumption
Bibliographic record
Abstract
Urban public space is obviously a key site of host-immigrant encounter. The heated debates in Europe about the establishment of purpose-built mosques or in Canada about monster houses show the deeper impact of changes in the urban streetscape consequent upon immigration. The establishment of ethnic shopping malls or commercial precincts, such as Chinatown or Klein Turkei, with their specific shop windows, street furniture and the whole shebang, is another, perhaps more promising case. The proliferation of these precincts is interesting because it is—at least partly—driven by commercial intentions and ties in with the emerging service economy and the role of cities as sites of consumption. The commodification and marketing of diversity, i.e. the commercial use of the presence of the ethnic Others or their symbols in the urban streetscape, help explain the growing enthusiasm for ‘interesting ’ landscapes that have the potential to draw tourists and visitors. This transformation is not a ‘natural ’ process, but the product of social, cultural, economic and political developments and conditions. This presentation examines the transformation of ethnic neighborhoods into places of leisure and consumption by a wider public in a number of cities and countries, and deals with the question of how and under what conditions this process helps foster immigrants ’ business success and the quality of the neighborhood at large. The primary focus is the role of immigrant entrepreneurs and their interaction with other relevant actors, especially the local government.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".