Canada’s National Housing Strategy: A suitable case for Australian emulation?
Bibliographic record
Abstract
This paper concerns the task of national housing strategy-making in two similarly developed federal states, Canada and Australia. Strategies help to define priorities and to provide a rationale for ongoing decision-making. Strategic thinking is the antithesis of an incremental or reactive approach. For the UN study #Housing2030 (UN 2021), strategic action on housing follows a logic which begins with causal analysis, informing the selection of relevant policies and their design, which are in turn resourced via capable implementation, and adapted following evaluation. The challenge of national housing strategy-making is substantially compounded in countries like Canada and Australia where housing powers and responsibilities are primarily accorded to state or provincial administrations rather than to federal authorities. In this paper we investigate Canada’s first-ever venture of this kind, its 2017 National Housing Strategy (NHS). A key focus is the relevance of the NHS for Australia, likewise a country with little recent history of national housing policy leadership, but with a recently elected federal government pledged to develop a formal 10-year plan. Our underlying research involved documentary analysis and interviews with Canadian housing policy stakeholders, with the current paper complementing and extending the coverage of our earlier research report (Martin et al. 2023). Constituting a form of knowledge exchange, that report informed the development of a bill to legislate Australia’s National Housing and Homelessness Plan tabled in Australia’s federal parliament in 2024. Cet article porte sur l’élaboration d’une stratégie nationale en matière de logement dans deux États fédéraux développés de manière similaire, le Canada et l’Australie. Les stratégies aident à définir les priorités et à fournir une justification pour la prise de décision. La pensée stratégique est l’antithèse d’une approche incrémentale ou réactive.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.028 | 0.014 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".