Bibliographic record
Abstract
Every night, about 800 000 Americans and tens of thou-sands of Canadians sleep on the street or in homelessshelters.1,2 Countless more stay temporarily with friends or relatives because they have no home of their own.3 Those who experience homelessness represent a remarkably diverse group: of the 32 000 people who used homeless shel-ters in Toronto in 2002, 15 % were families with children, 22 % were street youths between the ages of 15 and 24 years, 18 % were single women and 48 % were single men.4 Most people use homeless shelters for a period of weeks to months before finding housing in the community, but some 10%–20 % of shelter residents are chronically home-less.5,6 These people, for whom the shelters are a kind of semipermanent home, suffer from high rates of alcohol and drug addiction.5 Fortunately, substance abuse in homeless people is amenable to intervention. A recent systematic review has
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 | 0.010 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.036 | 0.017 |
| Insufficient payload (model declined to judge) | 0.121 | 0.037 |
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".