Ain't Talkin' ‘Bout Gentrification: The Erasure of Alternative Idioms of Displacement Resulting from Anglo‐American Academic Hegemony
Bibliographic record
Abstract
Abstract The concept of gentrification has become stretched, both conceptually and geographically, in ways that both erode its utility and displace alternative ways of understanding the displacement of lower‐income people by urban transformation. Among the negative consequences that we consider is that the resulting pressure to reframe analysis in terms of gentrification reinforces Anglo‐American academic hegemony and increases the difficulty of introducing more appropriate theoretical approaches from scholars in, and of, the global South. We draw on the anthropological concepts of emic and etic analysis to illustrate the dangers of such erasure and displacement of alternative frames of understanding. At the same time, the theoretical extension of the concept of gentrification to replace alternative ways of thinking about the displacement of lower income populations, such as ‘urban renewal’, has reduced the analytical utility of gentrification itself by confusing different mechanisms by which this is achieved. We illustrate the problems through consideration of the sustained tradition of work on displacement in Hong Kong using other conceptual frames. We encourage greater openness to alternative critical traditions that offer insights into the dislocation of poorer urban populations.
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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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.054 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".