Bibliographic record
Abstract
Introduction1 Willison considers Housing First to be the evidence-based response to homelessness. 2 The federal government was an important partner for provinces in the postwar era, and when the federal government left the field of housing, eight out of ten provinces followed suit.3 There is overlap between these groups, of course; third-sector groups include Indigenous-led organizations, and Indigenous-led groups are also involved in networks to administer federal funding, for example.The same can be said for third-sector groups that are not Indigenous-led; they are considered to be third-sector actors but also are involved in networks to administer federal funding.4 Not all actors are necessarily involved in homelessness governance in each case.The municipality is minimally involved in Calgary, the province is minimally involved in Ontario, and Indigenous actors are minimally involved in Montreal.But in each case, I looked at the actions of these actors or of groups representing them.5 In this book, I generally use people first language (person who is or who has experienced homelessness), but in some cases I use identity first language especially when that is the language used by people interviewed.There are debates regarding these terms, and it is increasingly common to talk of people who are unhoused and underhoused.For a thoughtful discussion on people versus identity first language, see Withers 2021 and Prince 2009.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.540 | 0.248 |
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".