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Record W4400779415 · doi:10.31857/s0132162524030077

Prevalence of homelessness in Russia: an assessment based on a retrospective survey

2024· article· en· W4400779415 on OpenAlexaboutno aff
Елена Цацура, Alexandra A. Osavolyuk

Bibliographic record

VenueSotsiologicheskie issledovaniya · 2024
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsnot available
FundersEuropean Commission
KeywordsPolitical scienceGeography

Abstract

fetched live from OpenAlex

This paper assesses the prevalence of street and hidden homelessness in Russia. Based on the representative survey “Person, Family, Society – 2023” (N = 9508 people) it was revealed that 4.6% of the adult population had experienced street homelessness, while 13.5% had faced hidden homelessness. 4.8% of respondents reported about suffering from homelessness for more than 1 year, including 2.5% with experience of more than 3 years of homelessness. Street homelessness problem is primarily for males, however the prevalence of hidden homelessness is high among women too. Factors such as male gender, low median income, and smoking increase the likelihood of homelessness in the past 10 years, while marriage, higher education, and children under the age of 18 in the household decrease this likelihood. Federal district of residence and type of settlement are not correlated with the experience of homelessness. Frequent alcohol consumption significantly increases the likelihood of street homelessness. Those who have experienced homelessness have unstable housing conditions with the expectation of ones future deterioration. They are more likely to live in communal apartments, hostels, and rented housing. The experience of homelessness is associated with lack of housing owned by the person. The experience of homelessness is closely related to feelings of loneliness and conflicts in the family. Homelessness is correlated with life dissatisfaction. Compared to several countries (Australia, Canada, European countries), Russia has a higher life-time prevalence of homelessness and longer average duration of homelessness. All this emphasizes the importance of housing market policies development and social protection of people vulnerable to 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 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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.073
GPT teacher head0.462
Teacher spread0.388 · 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 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

Citations0
Published2024
Admission routes1
Has abstractyes

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