Housing affordability, market interventions, and policy platforms in the 2022 Ontario provincial election
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Since the Great Recession, many cities around the world have undergone extreme demographic changes as people and capital resettle into urban areas. This has resulted in issues of gentrification and displacement forcing many governments to address growing concerns of housing insecurity. Housing policy is a function of political ideologies and social conditions drawing from market‐based housing supply (MBHS) solutions or demand‐side interventions (DSI) to alleviate housing cost burdens. Yet, debates on their effectiveness have often undermined their ability to grow to scale leaving many households in precarious housing situations. This paper focuses on the 2022 Ontario provincial election to uncover how Canadian political parties frame housing insecurity and their policy platforms. This paper finds all political parties promote the MBHS framework, yet various degrees of the DSI framework. Embedded within this variation are questions of federalism with responsibility shifting between provincial and municipal governments. The findings reveal while different forms of neoliberal ideology inform the policy platforms of political parties, federalism plays a significant role in framing the level and scale of government involvement.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it