Lessons from the first round of mandatory housing needs reporting in British Columbia, Canada
Bibliographic record
Abstract
Mirroring global trends, housing is a growing issue in urban Canada. To address the looming crisis, housing needs reports (HNRs) have recently gained traction with the promise to improve policy through data. While literature and practice have yet to coalesce around appropriate definitions and methods, the federal government is set to make HNRs national policy in 2025. In this study, we analyze a sample of 126 municipal HNRs produced by 2022 through a British Columbia mandate. Asking about the data reporting outcomes and policy lessons, we find high levels of compliance, achieved largely by relying on external consultants and private data. Lack of methodological guidance and uneven data availability particularly affect reporting on population projections and housing stock (changes). We call for capacity building and iterative evaluations to enhance the effectiveness of HNRs and increase policy alignment with the National Housing Strategy.
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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 | 0.041 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".