Bibliographic record
Abstract
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.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.027 | 0.076 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".