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Record W1510923079 · doi:10.1002/jcop.21665

HOW STABLE IS STABLE? DEFINING AND MEASURING HOUSING STABILITY

2014· article· en· W1510923079 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Community Psychology · 2014
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsUniversity of TorontoDalhousie UniversityCentre for Addiction and Mental Health
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsFinancial stabilityHousing FirstHousing tenureStability (learning theory)Quality (philosophy)Scale (ratio)BusinessActuarial sciencePsychologyPublic economicsDemographic economicsEconomicsGeographyComputer science

Abstract

fetched live from OpenAlex

Despite housing stability being a key concept in housing and homelessness policy, research, and service provision, it remains poorly defined and conceptualized, and to date there are no standard measures. We use in‐depth qualitative interviews with 51 young people transitioning away homelessness over the course of a year to examine the core dimensions of housing stability. Due to the potential for sudden change, we define housing stability as the extent to which an individual's customary access to housing of reasonable quality is secure. We define housing security among 8 main dimensions: housing type, recent housing history, current housing tenure, financial status, standing in the legal system, education and employment status, harmful substance use, and subjective assessments of housing satisfaction and stability. Based on these dimensions, we suggest a brief 13‐question scale that measures housing security.

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.

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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.183
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.196
GPT teacher head0.447
Teacher spread0.251 · 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