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Record W4399282058 · doi:10.3390/youth4020052

Unpacking the Discourse on Youth Pathways into and out of Homelessness: Implications for Research Scholarship and Policy Interventions

2024· article· en· W4399282058 on OpenAlexaffabout
Ahmad Bonakdar

Bibliographic record

VenueYouth · 2024
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsYork University
Fundersnot available
KeywordsUnpackingScholarshipPsychological interventionSociologyPolitical sciencePsychologyLinguisticsLaw

Abstract

fetched live from OpenAlex

Youth homelessness presents a complex and persistent challenge worldwide, particularly affecting young adults between 16 and 24 years of age in the US and Canada. This population faces elevated risks of exploitation, victimization, and various health issues upon detachment from familial support structures. Understanding the multi-faceted nature of youth homelessness requires the consideration of individual, structural, and systemic factors within the socio-ecological model. Historically, when examining youth homelessness, traditional methods have concentrated either on individual factors contributing to homelessness or on broader structural issues within society. The emergence of the new orthodoxy attempted to bridge the apparent gap between individual- and structural-level factors by considering both to be equally significant, but it faced skepticism for its theoretical framework. In response, the “pathways” approach gained traction, emphasizing the subjective experiences and agency of youth experiencing homelessness. Departing from conventional epidemiological models, the pathways approach views homelessness as a dynamic process intertwined with individual life contexts. This paper navigates the scholarly discourse on youth homelessness and examines the distinct characteristics of the pathways approach. By exploring its implications for research and policy, this study contributes to a nuanced understanding of youth homelessness and informs future prevention-focused interventions.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.365
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.411
GPT teacher head0.567
Teacher spread0.155 · 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 teacher head, not a consensus.

Study designQualitative
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

Citations2
Published2024
Admission routes2
Has abstractyes

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