Introduction: gentrification, social mix/ing and mixed communities
Bibliographic record
Abstract
This chapter discusses the international scope and increasing prominence of social mix policies that enact processes of gentrification worldwide. It argues that the literatures on social mix and on gentrification, have, until now, existed as parallel discourses, and that there is an urgent need to read them together. The introduction begins by discussing the history of social mix policy and rhetoric, and by assessing, given the recent focus on social capital, if/how the meaning of social mixing has changed over recent decades and if we now have different expectations of what might constitute a socially mixed community. It moves on to look at the proliferation of gentrification and social mix in different national contexts. The countries that the chapter discusses represent the spectrum of policy contexts in which social mix is an explicit policy intervention, one viewed as welfare enhancing (Canada), through to different levels of policy intervention that seek to steer market processes towards mix (European cases), through to more marketized interventions (the USA and Australia). Then turning to the gentrification literature, the chapter discusses the evidence about whether social mix is but a transitory phenomenon on the way to complete gentrification (social homogeneity). It considers whether gentrifiers are more predisposed towards social mixing than other members of the middle class. And finally turning to the social mix literature, the chapter considers what the adequate threshold of social interaction might be to justify an area being regarded as socially mixed. And importantly, it questions whether the aspirations of social mix policy sit well with the lived realities of daily conduct by different social groups.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.026 | 0.004 |
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".