Appendix A Impact of Homelessness on Children: An Analytic Review of the Literature
Bibliographic record
Abstract
This paper reviews published research conducted in the United States pertaining to the effects of homelessness on the mental health, behavior, health, and academic performance of children who are homeless with their families. This has been the central aim of most of the studies involving homeless children that have been conducted to date. A primary intent of the chapter is to describe what has been learned as well as to discuss some of the issues that may have led to inconsistent study findings over the years. In addition, the paper identifies gaps in the understanding of homeless children, one of which is the lack of information on different subgroups of homeless children based on varying constellations of problems or needs. Part I: Literature Review Using data from the National Survey of Homelessness Assistance Providers conducted in 1996, The Urban Institute (2000) estimated that families with children account for about 39 percent of the homeless population in this country on any given night.1 Based on this survey, researchers at The Urban Institute estimated that somewhere between 874,000 and 1,360,000 children experienced a homeless episode2 at some point in 1996. This implies that about 9 percent of poor children in the United States had a spell of homelessness that year. In most cases, a homeless family is comprised of a single mother with
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.021 | 0.026 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.062 | 0.005 |
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".