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Record W2346212796 · doi:10.13140/rg.2.1.4067.9281

All Alone: Antecedents of Chronic Homelessness

2015· article· it· W2346212796 on OpenAlexaboutno aff
Daniel John Flaming, Patrick Burns

Bibliographic record

VenueSSRN Electronic Journal · 2015
Typearticle
Languageit
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsnot available
Fundersnot available
KeywordsPopulationPublic healthQuarter (Canadian coin)Public assistanceMental healthPsychologyGerontologyMedicinePsychiatryEnvironmental healthPolitical scienceNursingGeographyWelfare

Abstract

fetched live from OpenAlex

Public assistance programs are the primary interface with individuals experiencing homelessness. Roughly a quarter of LA County’s population receives some form of public assistance, including those who are homeless or who are at risk of homelessness. Public assistance programs can be a catalyst for connecting at-risk and homeless recipients with crucial services and reducing the massive public costs associated with chronic homelessness. The vital role is to identify tripwire events among all recipients, particularly children and transition-age youth, and quickly connect at-risk individuals with needed employment, behavioral health and housing services provided by other organizations.Extended participation in cash benefit public assistance programs is more frequent among recipients with childhood experiences of homelessness. Experiences of homelessness while transitioning from childhood to adulthood are associated with reduced employment rates and highly elevated rates of disabilities for both women and men. Mental health and substance abuse services are scarce for the population of single adult males with extended dependence on public assistance, which is at highest risk of chronic homelessness.

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)0.000
metaresearch head score (Gemma)0.003
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 score0.025
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0090.000

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 head0.042
GPT teacher head0.374
Teacher spread0.332 · 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

Citations1
Published2015
Admission routes1
Has abstractyes

Explore more

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