Bibliographic record
Abstract
Over the last quarter century, homelessness has become one of the symbols of urban blight.Regardless of the accuracy of this perception, homelessness is indeed a serious issue in many cities.More than 800,000 Americans are homeless in a given week, and 3.5 million are homeless over the course of a year (Burt, 2001).About 2-3% of the U.S. population, or 5-8 million people, have experienced at least one night of homelessness in the past five years (Link, et al., 1994).About 70% of homeless people in the U.S. live in urban areas (Burt, 2001).Within the countries of the European Union, estimates of the number of homeless people in 1997 were 580,000 in Germany, 166,000 in the United Kingdom, 30,000 to 40,000 in the Netherlands, 10,000 in Finland, 8,000 in Sweden, and 6,000 in Norway (Menke, et al., 2003).Contrary to stereotypes, a broad range of people experience homelessness, including not only single men, but also single women, runaway adolescents, and families with young children.In the U.S., these subgroups represent about 60%, 16%, 9%, and 15% of the homeless population, respectively (Burt, 2001).In the European Union, substantial numbers of homeless families with children are found only in Germany and the United Kingdom (Menke, et al., 2003).Here and throughout this chapter, we define homeless people as individuals who lack a fixed, regular, and adequate night-time residence, including those who are living in emergency or transitional shelters, in motels or hotels due to lack of alternative adequate accommodations, or in private or public places not intended for human habitation (such as cars, parks, public spaces, abandoned buildings, or bus or train stations).The health of the homeless and the role of cities in their health present an important challenge.The relationship between urban living and the health of the homeless raises two intertwined questions.First, how does the urban environment influence the creation and perpetuation of homelessness, especially among (but not limited to) individuals with pre-existing health problems such as mental illness and substance abuse?Second, how does the urban environment affect the health of people after they have become homeless?At first glance, these two questions appear almost identical in terms of the specific characteristics and attributes of the urban
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.047 | 0.018 |
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".