Social policies, financial markets and the multi-scalar governance of affordable housing in Toronto
Bibliographic record
Abstract
While housing has been a central object of financialisation, questions regarding how multi-scalar states shape the financialisation of housing remain under-researched. I address this knowledge gap through a case study of the financialisation of affordable housing in Toronto. By analysing pertinent policy documents, I examine the roles and relationship of the federal, provincial and local states in the financialisation of affordable housing. Two findings are highlighted. (1) Although policies from all levels of government show traits of financialisation – in terms of both the connection between social policy and financial markets, and financialised ideologies prevailing in policy discourses, the extent and pattern of the manifestation of financialisation are distinct. This research thus calls for a nuanced understanding of the state’s role in the financialisation of housing from a multi-scalar perspective. (2) Affordable housing policies usually do not give an explicit definition of ‘affordable’. By scrutinising the policy specifications, I found that the target group is mainly moderate-income, rather than low-income, households. It will be increasingly difficult for low-income households to meet their housing needs.
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".