Suburban Migration: Interrogating the Intersections of Global Migration and Suburban Transformation
Bibliographic record
Abstract
Abstract Suburbanisation as a global phenomenon has presented multifaceted patterns of evolution and transformation in various contexts. Migrant settlements in suburban spaces just add more complexities to suburbia by bringing diverse demographics, (inter)cultural practices, new built forms, and new meanings of space and community. These migrant spaces challenge conventional suburban socio-spatial organisations of land, infrastructure, and resources as well as suburban governance, planning, and design. The manifestations of migrant suburbs where diversity and urban growth are juxtaposed inevitably present profound implications for governments, practitioners, and academics in a myriad of ways, such as changing land uses and physical forms (e.g. neighbourhood characters), competing claims for space and rights to the city (e.g. who has the access), and increasing awareness of equity and social inclusion (e.g. who belongs to and in the community). This chapter draws on the migration-related suburbanisation processes in different contexts and applies the theory of the production of space to cast light upon the narratives of everyday suburban life, diversity management, growth and development, policy and governance, and socio-spatial (in)equity and (in)justice.
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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 | 0.002 | 0.010 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".