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Record W2141388528 · doi:10.1177/0002764207311983

Homelessness, Children, and Youth: Research in the United States and Canada

2008· article· en· W2141388528 on OpenAlexaboutno aff
Darcy Varney, Willem van Vliet

Bibliographic record

VenueAmerican Behavioral Scientist · 2008
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsnot available
Fundersnot available
KeywordsVariety (cybernetics)Youth studiesPositive Youth DevelopmentPopulationWork (physics)Economic growthCriminologyPolitical scienceSociologyPsychologyPublic relationsDevelopmental psychology

Abstract

fetched live from OpenAlex

This issue of American Behavioral Scientist makes available some of the most recent research on the growing social, economic, and human development impacts of homelessness on families—specifically, on the lives of children and youth. The seven studies from the United States and Canada compiled here provide important evidence-based insights to inform efforts aimed at combating homelessness among children and youth. They represent a variety of methodologies, including rigorous, person-centered approaches that reveal the complexity of the homeless experience for young people and the heterogeneity of the young homeless population. As a body, the studies highlight the importance of understanding the diverse contexts in which homeless children and youth live and tailoring supportive services accordingly. Thousands of homeless young people remain unrecognized and underserved. The work in this issue illustrates the urgency of bringing researchers, policy makers, and practitioners together to work toward adequate and affordable shelter for all.

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 imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.061
Threshold uncertainty score0.446

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.012
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0080.024
Science and technology studies0.0170.004
Scholarly communication0.0070.002
Open science0.0020.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.108
GPT teacher head0.438
Teacher spread0.330 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations17
Published2008
Admission routes1
Has abstractyes

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