Decommodification Now: Planning for a decommodified housing future
Bibliographic record
Abstract
Canada is experiencing a housing affordability crisis. Rising housing costs in cities over the last two decades have driven increasing gentrification and displacement, forcing lower-income residents into inadequate and unaffordable housing, or out of cities altogether. The COVID-19 pandemic has only worsened this phenomenon, as evictions, homelessness, and number of households in core housing have risen sharply over the past year. These interlocking issues are underpinned by a single idea: that housing is a commodity. This notion holds that housing is both a store of value, and a necessity. This tension is usually resolved in favour of building housing that can generate maximal capital for its investors, as opposed to housing that serves community need. This creates the conditions leading to nationally increasing core housing need. A reorientation of housing planning and policy around the idea that housing is a necessity outside of the drive for profit is required. One promising avenue for this revisioning is decommodification. This project seeks to answer (1) how decommodified housing has existed in Canada in the past, (2) what kinds of decommodified housing exist, and are currently being produced in peer nations, and (3) what are the existing barriers and opportunities to greatly expand the stock of decommodified housing in Canada.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".