Transnational gentrification: The crossroads of transnational mobility and urban research
Bibliographic record
Abstract
This introduction to the special issue introduces the contributors’ articles and identifies key themes relating to how increased transnational mobility has affected urbanisation processes in many cities, resulting in the globalisation of rent gaps. A mix of local and transnational real estate interests work to attract higher-income lifestyle migrants and tourists, often from higher-income countries to lower-income urban space in order to increase its exchange value. In the process, however, they act to reduce the use value of urban space to lower-income residents. The introduction notes that the acceleration of lifestyle mobilities moving through urban spaces, and the development of transnational lifestyles of urban place consumption, have produced new forms of gentrification – not merely the spread of an urban strategy to new cities, but the planetarisation of rent gaps. Transnational gentrification is the form of contemporary urbanisation that occurs as a result of closing these rent gaps through attraction of higher income, transnational migrants, often from high-income countries in Northern Europe and North America.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 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".