Bibliographic record
Abstract
This paper investigates the way in which the Housing First philosophy and model have been adapted to suit the setting in Calgary and Alberta. It also touches on the relationships between different stakeholders in the realm of homeless service delivery in Alberta, as well as incentives these stakeholders have to reach common goals. The Alberta government has pledged hundreds of millions of taxpayer dollars to end homelessness through adoption of Alberta's 10-year plan to end homelessness. Calgary has adopted a similar plan. Both plans commit to end homelessness in Alberta and Calgary, respectively, by 2018 by employing a strategy known as "Housing First". Housing First is a fairly new and revolutionary philosophy and service delivery model in which clients experiencing homelessness are offered a permanent housing solution before any other needs, such as mental health, addictions, poor employability, etcetera are addressed. While Housing First has been widely acclaimed in the academic literature, it has only been so in the context of single men and women with a diagnosis severe mental illness. There is no literature proving its efficacy or effectiveness for other populations such as families with children. However, the plans in Calgary and Alberta more widely call for Housing First to be applied to all populations. As time passes and more data is collected, we will see whether this wide adoption of Housing First is hailed as an innovative and forward-thinking or premature on the part of the government and the Calgary Homeless Foundation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".