Gentrification or …? Injustice in large-scale residential projects in Hanoi
Bibliographic record
Abstract
Large-scale residential developments on expropriated lands in periurban Hanoi resemble forms of gentrification seen elsewhere. But is it gentrification? Current debate over the definition of gentrification has focused on whether the term has become too broad to be useful in different institutional and spatio-temporal contexts. While some push for a generalisable definition based in capitalist development, others argue that the term harbours Western assumptions that fail to usefully explain unique local circumstances. The paper first identifies one such conceptual assumption that must be made explicit since it provides the term’s politicising thrust: displacement generates an experience of social injustice. Then, drawing on surveys and interviews with residents as well as interviews with real estate agents, government officials and academics conducted in Hanoi between 2013 and 2017, the paper evaluates five types of displacement on the city’s outskirts. Because displacement only occurs in marginal cases and generates limited feelings of social injustice, the term ‘gentrification’ is of little use. Instead, the paper suggests that in a context of rapid urbanisation and relatively inclusive economic growth such as that of Hanoi the terms ‘livelihood dispossession’ and ‘value grabbing’ may better capture the experience of social injustice and are therefore more likely to generate political traction.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".