New directions in transnational gentrification: Tourism-led, state-led and lifestyle-led urban transformations
Bibliographic record
Abstract
Transnational gentrification is class-based neighbourhood change driven by relatively affluent international migrants. In contrast to the conventional globalisation narrative in which people are significantly more place-bound than capital flows, transnational gentrification suggests that a globally mobile capitalist class has been in large part responsible for rapid change in many urban neighbourhoods. Observations of transnational gentrification have accelerated over the past decade, with scholarly accounts reporting on cases in disparate locations – particularly those in Latin America and the Mediterranean with ‘charming’ old-world architecture, significant cultural amenity and rents below OECD averages. In this article we attribute transnational gentrification in the 21st century to three primary drivers: new forms of tourism and short-term rentals; state-led initiatives to revitalise urban neighbourhoods and catalyse economic activity; and lifestyle-driven migration and new forms of consumption. We argue that transnational gentrification is not simply an outcome of a globalised ‘rent gap’ but instead a product of a new global residential imaginary coupled with enhanced possibilities for transnational mobility facilitated by digital platforms and state-led efforts to extract new forms of rent from particular neighbourhoods. We conclude by offering a number of potential avenues for future research, many of which resonate with key themes that emerged decades ago as gentrification first began to transform cities and urban policy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.024 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| 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".