What kind of issues will policymakers face whilst importing built for zero?
Bibliographic record
Abstract
Key stakeholders across the Global North are experimenting with data policies and practices to efficiently end homelessness. Built for Zero is an approach created in the USA that uses complete and timely data to make homeless systems pliable to fluid population dynamics. Community Solutions is nongovernmental organization from the USA that created Built for Zero. Advocate groups in Australia, Canada, Denmark, England, and France are now importing this methodology to homeless systems in their country. Importing Built for Zero is complicated because it was designed for US homeless systems. As a result, its key components are tailored to social, economic, and political conditions that are unique to that country. To date, housing analysts have only started to evaluate the implementation and impact of Built for Zero. Within that small literature, no one has considered issues that policymakers outside of the USA will face whilst trying to import Built for Zero. This paper starts that conversation by analyzing problems that English councils adopting this approach will likely confront. The author identifies key components of Built for Zero that must be adapted to UK homeless statutes and poses questions for policymakers in other countries to answer whilst they import Built for Zero.
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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 | 0.061 | 0.103 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.018 |
| Scholarly communication | 0.029 | 0.029 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.024 | 0.019 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".