Mobilizing a fast policy fix: Exploring the translation of 10-year plans to end homelessness in Alberta, Canada
Bibliographic record
Abstract
The management of homelessness has taken various forms over time. In 2003, the U.S. federal government significantly shifted its approach, ambitiously committing to end homelessness within 10 years by targeting the chronically homeless using the Housing First model. This approach to homelessness has rapidly spread across North America and beyond. This article is concerned with how the mobility of these 10-year plans has been realized. Drawing on Peck and Theodore’s concept of “fast policy,” and borrowing perspectives developed in actor-network theory, the article develops a case study of Alberta, Canada, to chronicle how 10-year plans were translated through a dense network of political alignments, socio-technical expertise, and statistical inscriptions. A close examination of these translations invites us to problematize this socio-technical infrastructure as a powerful mode of adaptive governance closely associated with the dynamism of neoliberalism itself.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.023 | 0.011 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".