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Record W1970909889 · doi:10.2202/1944-2858.1041

Best Practices in Policy Creation and Administration in Meeting Housing Needs for People with Disabilities and Their Families: A Case Study in Ottawa County, Michigan

2010· article· en· W1970909889 on OpenAlexaboutno aff
Virginia Beard

Bibliographic record

VenuePoverty & Public Policy · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicUrban and Rural Development Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsParticipatory action researchWork (physics)Citizen journalismPublic administrationAdministration (probate law)Public relationsPolitical scienceEconomic growthBusinessPsychologyEconomicsLaw

Abstract

fetched live from OpenAlex

Abstract For people with disabilities across the spectrum of disability categories, there is a need for better understanding of current and projected housing needs options. What housing, programs and policies exist to serve the disability community? How are policies and programs for people with disabilities, their families and caregivers being implemented? What gaps remain in the knowledge about housing and disability as well as in policy and implementation/administration arenas surrounding housing for people with disabilities and their families? Through a participatory action process, this project seeks to improve understanding and policy and program recommendations on housing options for the disability community. The topics identified as central to housing for the disability community that need better understanding include affordability of housing, accessibility, available individual supports and access to community amenities, such as transportation, that exist or are lacking. Through review of current data and a collection of new data in the form of a case study from Ottawa County, Michigan, this project will work to improve understanding and create a set of recommendations regarding housing options for people with disabilities, their families, and their caregivers.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.592
Threshold uncertainty score0.633

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.053
GPT teacher head0.348
Teacher spread0.295 · 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

Citations2
Published2010
Admission routes1
Has abstractyes

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