Gentrification in<scp>H</scp>ong<scp>K</scp>ong? Epistemology vs. Ontology
Bibliographic record
Abstract
Abstract This article examines the transferability of the concept of gentrification away from itsAnglo‐American heartland to the cities ofAsia Pacific and specificallyHongKong. An epistemological argument challenges such theoretical licence, claiming that conceptual overreach represents another example ofAnglo‐American hegemony asserting the primacy of its concepts in other societies and cultures. Past research suggests that if gentrification exists inAsia Pacific cities it bears some definite regional specificities of urban form, state direction and, most surprising from a Western perspective, a potentially progressive dimension for some impacted residents. Closer examination of urban discourse inHongKong is conducted through analysis ofEnglish andChinese language newspapers. In both instances, gentrification is barely used to describe the pervasive processes of urban redevelopment, which otherwise receive abundant coverage. Interviews with local housing experts confirm the marginality of gentrification in academic and public discourse, and the power of a local ideology that sees urban (re)development unproblematically as a means of upward social mobility. However, in the decade‐long housing bust after 1997, growing inequality has encouraged a nascent class analysis of the property market, an ontological awakening that may prove more favourable to the identification of gentrification in anAsia Pacific idiom.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.049 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 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".