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Record W2246640443

A Window Into Park Life: Findings From a Resident Survey of Nine Mobile Home Park Communities in Vermont

2011· article· en· W2246640443 on OpenAlexvenueno aff
Daniel Baker, Kelly Hamshaw, Corey Anne Beach

Bibliographic record

VenueJournal of rural and community development · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicUrban, Neighborhood, and Segregation Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAffordable housingEconomic growthSocioeconomicsBusinessRural housingPlan (archaeology)Quality of life (healthcare)Rural areaGeographyPolitical scienceSociologyPsychologyEconomics
DOInot available

Abstract

fetched live from OpenAlex

Affordable housing is a critical issue facing many rural communities. Mobile home parks are a form of affordable housing prevalent in rural areas that is being lost as parks close across the United States and few new parks are developed. Limited research on how residents view their parks and the public perception that mobile home parks are undesirable may diminish efforts to prevent park closures. This paper considers how mobile home residents view their quality of life and housing issues living in this type of rural community. Findings from a survey of two hundred and fifty-six residents at nine mobile home parks in Vermont are reported. Respondents were asked how they viewed their housing, neighborhood, park management, and infrastructure. Residents were found to have lower incomes than other households in the state. Most were satisfied with life in the park, though lower income residents were significantly happier with the social atmosphere than those with higher incomes. For many households mobile home parks are not transitional housing but are rather places they have lived, and plan to live, for many years. This research can aid rural planners improve and develop future affordable housing, including the evolution of the mobile home park. Keywords: affordable housing, manufactured housing, mobile home parks, rural communities

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.189
Threshold uncertainty score0.824

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.074
GPT teacher head0.284
Teacher spread0.210 · 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 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

Citations17
Published2011
Admission routes1
Has abstractyes

Explore more

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