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Record W3209637288 · doi:10.5206/ijoh.2021.1.13629

What is the Problem of Homelessness in Japan? Conceptualisation, Research, and Policy Response

2021· article· en· W3209637288 on OpenAlexvenueno aff
Masami Iwata

Bibliographic record

VenueInternational Journal on Homelessness · 2021
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsnot available
Fundersnot available
KeywordsPremisePublic policyPolitical sciencePerceptionAllowance (engineering)PandemicSociologyEconomic growthDevelopment economicsCoronavirus disease 2019 (COVID-19)PsychologyLawEconomicsMedicine

Abstract

fetched live from OpenAlex

The definition of homelessness and the policies responding to it differ from society to society, as does understanding a state of being "homeless", all of which are socially constructed. Because in Japan today homelessness is perceived only as a situation of sleeping on the street, the prevalence is perceived as low. However, this definition is narrow compared to what, for example, FEANTSA has proposed in Europe. Therefore, Japanese policy and policy makers need to shift to be congruent with international standards. To begin this shift we need to understand how the "narrow" perception of homelessness in Japan has been constructed. Therefore, in this paper a historical review is provided starting on the premise of the "loss of home" of Japanese society after World War II, the rapid increase of "visible homelessness" since the 1990s, the enactment of formal homelessness law, and rising "visible homelessness". More recently there is also expanded interest in "invisible homelessness" due to current homelessness research. The issue of lack of a public housing allowance and limited public housing is explored connected to an absence of housing policy. Finally, the Covid-19 pandemic has increased anxiety about the loss of homes and there is a need to shift homelessness measures into housing policy.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.187
Threshold uncertainty score0.656

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.100
GPT teacher head0.478
Teacher spread0.377 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations9
Published2021
Admission routes1
Has abstractyes

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