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Record W4383811724 · doi:10.56687/9781847424952-009

Social mixing and the historical geography of gentrification

2011· book-chapter· en· W4383811724 on OpenAlexaboutno aff
David Ley

Bibliographic record

VenuePolicy Press eBooks · 2011
Typebook-chapter
Languageen
FieldSocial Sciences
TopicFrench Urban and Social Studies
Canadian institutionsnot available
Fundersnot available
KeywordsGentrificationNeighbourhood (mathematics)IdeologyPolitical economyWelfare statePublic housingFraming (construction)SociologyPolitical scienceGeographyPoliticsEconomic growthLawArchaeologyEconomics

Abstract

fetched live from OpenAlex

This chapter discusses the ‘moral landscape’ of False Creek South in Vancouver, Canada, a neighbourhood that was redeveloped in the 1960s and 1970s by planners enacting their vision of the liberal, livable city. False Creek South was built as a socially and tenurially mixed neighbourhood on the south shore of False Creek in downtown Vancouver. The chapter discusses the ideas/ideologies behind its conceptualisation as a socially mixed neighbourhood, describing and evaluating the development, and discusses its successes and failures re. social mixing. The final part of the chapter considers la longue durée with respect to gentrification and social mixing. Among student radicals, artists and young professionals in the 1960s and 1970s, social mixing was politically progressive. By the 1970s, it was being institutionalized by left-liberal governments in the last hurrah of the welfare state (False Creek South), when funds for social programmes were relatively plentiful. The fiscal crisis of the state and a new conservative consensus put an end to all that in the neo-liberal 1980s, an era that is only now collapsing under its own contradictions. But today social mixing is vilified in some quarters as an underhand strategy of a conspiratorial state to displace the poor. What has changed? Social mixing or our framing of it? To answer this, the chapter underscores the progressive intent of inner city social mixing in the 1960s–1970s and then projects that argument against the critical response in the present. Its sub-theme is that as gentrification turns 50, we can profitably learn from some historical comparison. To do so, we need to preserve a lively memory of intellectual legacies.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.773
Threshold uncertainty score0.918

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.112
GPT teacher head0.286
Teacher spread0.174 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreOther

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".

Quick stats

Citations1
Published2011
Admission routes1
Has abstractyes

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