Housing affordability: a major problem for many cities
Bibliographic record
Abstract
Housing affordability issues are confronting many cities, particularly those that are economically successful. For many, the gap between what people can afford to pay for housing and how much they earn is widening. The rising cost of buying or renting a dwelling located close to jobs and services is having an adverse impact not only on the economic productivity and competitiveness of cities but also on the social cohesion and wellbeing of many communities. What is emerging is increasing spatial patterns of locational disadvantage and inequality within cities. The challenge for cities, however, is not just about providing more dwellings. It is about delivering a range of dwelling types to meet different household needs and budgets and, importantly, in locations close to jobs, services and public transport. It is also about providing security of tenure, well designed good quality housing that is healthy and safe to live in and developing an infrastructure investment pathway that builds more social housing in locations where it is needed. This chapter examines the way cities like London, Melbourne, Vancouver BC and Berlin are dealing with housing affordability issues.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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".