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Record W2904641451

Evaluating A Typology Of Homelessness Across A Midwest State

2018· article· en· W2904641451 on OpenAlexaboutno aff
Devin M. Hanson

Bibliographic record

VenueHuman Biology · 2018
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsnot available
Fundersnot available
KeywordsTypologyState (computer science)SociologyPolitical scienceCriminologyComputer scienceAnthropology
DOInot available

Abstract

fetched live from OpenAlex

ABSTRACT EVALUATING A TYPOLOGY OF HOMELESSNESS ACROSS A MIDWEST STATE by DEVIN M. HANSON August 2018 Advisor: Dr. Paul Toro Major: Psychology (Clinical) Degree: Doctor of Philosophy Identifying a typology remains an effective method to summarize and distinguish the different ways that people experience homelessness in communities. More than twenty years ago researchers in the northeast United States developed an approach to create a typology of homelessness by using electronic records of shelter stays and two dimensions of homelessness; number of episodes, and length of time spent homeless. The three-part typology Randall Kuhn and Dennis Culhane identified has shaped the way researchers and policy makers conceptualize homelessness and what strategies are utilized to address it. Since that time other studies have used the same approach in searching for a typology in three municipalities in Canada. This study applies Kuhn and Culhane’s approach to a broader region with urban, suburban, and rural geographic and population centers. What is found is a remarkable similarity and consistency in the typology that arises in these regions, and consistency with previous work in varied settings (New York City, Philadelphia, Toronto, Ottawa, Guelph). Implications for the consistency of this typology twenty years later are discussed and a potential needed shift in approach to this effort are discussed.

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 categoriesInsufficient 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 score0.071
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.229
GPT teacher head0.580
Teacher spread0.352 · 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
Published2018
Admission routes1
Has abstractyes

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