Urban Cultural Diversity and Economic Migration in Austere Times
Bibliographic record
Abstract
Introduction Economic migration flows, accelerated by globalization, have substantially increased the cultural and ethnic diversity of Western societies with high GDP economies. As a large part of these migration flows are motivated by the aspirations of those living in the Global South, or the majority world, to improve their living conditions in more economically prosperous countries, the result in the host societies is not only a substantial increase in ethnic and cultural diversity, but also greater social challenges in accommodating difference as well as the policy challenges of addressing socio-spatial inequalities that already exist in cities. The rapid growth of inwards migration not only poses a formidable challenge from the point of view of intercultural relations, but also for the social and spatial cohesion of the destination societies. The resulting inequalities add to the racialized geographies of the early 21st century in many Western countries. The different kinds of migrants coming from the Global South – labour migrants, refugees, asylum seekers – and the places in which they concentrate, together with the local disadvantage created by histories of racism and colonialism of the last century, are amongst the most vulnerable to the dynamics of social marginalization and stigma. These dynamics have been exacerbated in many Western cities since the 2008 Global Economic Crisis (GEC) and the introduction of austerity policies discussed throughout the book. This chapter discusses the way that (neoliberal) austerity has impacted social, racial and cultural inequalities and the ability of collaboration to support more inclusive democratic cities or resist exclusions. The basic premise is that cities play a fundamental role in the dynamics of social inclusion or exclusion of economic migrants and other racial and ethnic minorities, and in the way that societies cope with the challenge of recognizing and accommodating cultural diversity. This is due to at least three phenomena: first, cities are the places where the vast majority of the newcomers settle and where the greatest cultural and racial diversity can be found; second, cities concentrate a wide array of services and infrastructures and are the site of policy that creates or removes opportunity for the social inclusion of migrants and minorities – for example schools, social services, health and community centres, sport and cultural facilities, parks and squares, urban revitalization, public transport or migrant settlement services;
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".