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Homelessness

2014· reference-entry· en· W4240623483 on OpenAlexaboutno aff
Lois M. Takahashi

Bibliographic record

VenueGeography · 2014
Typereference-entry
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsnot available
Fundersnot available
KeywordsPovertySocial exclusionVariety (cybernetics)Political scienceSociologyInequalityEconomic growthCriminologyDevelopment economicsEconomics

Abstract

fetched live from OpenAlex

Homelessness has been defined along a spectrum of insufficient and inadequate shelter, from literal street sleeping to sleeping in temporary shelters to overcrowded housing circumstances; along a spectrum of time, from continuous to sporadic homeless episodes; and along a spectrum of space, from limited mobility to movement within and across geographic areas. In the United States and the Western industrialized world, homelessness was often framed as a crisis in the 1980s but has since become part of a larger narrative concerning entrenched poverty and income inequality. Research in the 1980s, primarily in sociology, psychology, social work, public policy, urban planning, public health, and geography, focused on defining homelessness, identifying the multiple and intersecting causes of homelessness, clarifying mental health issues faced by homeless persons, and recommending strategies, including emergency shelters, transitional housing, permanent housing with and without social services, and housing with and without prerequisites. Early-21st-century research on homelessness has deepened scholarly and policy understanding of the variety of homeless subpopulations and their specific needs and survival strategies and increasingly has framed homelessness as a particular aspect of the larger structural issues defining poverty and inequality in industrialized countries. Geographers in particular have emphasized the spatial dimensions of homelessness and have provided a countervailing explanation (usually based in social structures such as poverty and social exclusion) to the popular notion that homelessness is the result of individual counterproductive behavior or vulnerabilities. This review article includes both conceptual and empirical research, endeavoring to cover the myriad of conceptual frameworks explaining homelessness and the varied approaches researchers have tested to ameliorate homelessness. The article also summarizes resistant threads of scholarship on homelessness, from theories of revanchism to feminism, and includes both work in the United States and other industrialized countries (in particular, Canada and the United Kingdom). The article is organized into the following sections, which highlight in particular the contributions made by geographers and those with spatial lenses. First, two overview sections summarize publications that have led the field in conceptualizing homelessness for scholars and policymakers; one of the sections highlights specific geographical contributions. A section on reference resources, especially online, follows that and focuses on advocacy organizations. The next few sections show how scholars have described the needs and challenges faced by varying groups of homeless persons, centering on health, mental health, and substance abuse. Geographers and others have worked to reconceptualize homelessness, from descriptions of need and individual deficits to a focus on systems and politics; one section highlights these innovative works. The final four sections summarize alternatives to the population descriptions that comprise the mainstay of homelessness research to focus on stigma and identity, revanchism and resistance, and conceptual and empirical discussions of policies and programs that should be, and have been, developed and delivered to address homelessness. I thank Nathaniel Barlow for expert research assistance, and the editorial team and two anonymous reviewers for their very helpful comments. All errors or omissions remain my responsibility.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.846
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.002

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 head0.039
GPT teacher head0.372
Teacher spread0.334 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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
Published2014
Admission routes1
Has abstractyes

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