What is the Problem of Homelessness in Japan? Conceptualisation, Research, and Policy Response
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".