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Record W2936718958 · doi:10.1007/s10901-019-09665-z

Housing plans of the oldest: ageing in semi-rural areas in Sweden

2019· article· en· W2936718958 on OpenAlexaboutno aff
Marianne Abramsson, Jan-Erik Hagberg

Bibliographic record

VenueJournal of Housing and the Built Environment · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Aging, and Tourism Studies
Canadian institutionsnot available
FundersLinköpings Universitet
KeywordsQuarter (Canadian coin)Human geographyGeographyPopulation ageingPopulationSocioeconomicsRural areaEconomic growthDemographyPolitical scienceSociologyEconomics

Abstract

fetched live from OpenAlex

A number of smaller municipalities have decreasing population rates. Generally, the young move out, increasing the proportion of older people. To increase our understanding of the living conditions of an ageing population in small municipalities, a postal survey was conducted in three small, semi-rural municipalities in southern Sweden. In the survey the respondents answered questions about their living situation and their housing plans. The aim of this study was to investigate the housing situation and housing plans of the very old in semi-rural areas and research questions analysed for this study concerned the current housing situation and plans for future housing. A total of 1386 surveys were sent out in March 2014, to all inhabitants aged 80 years or more, residing in the ordinary housing market in the three municipalities, the response rate was 60%. The results show that most of the respondents were firmly rooted in the area as most of them had lived in the municipality for more than 20 years and 60% had lived in their current dwelling for more than 20 years. Ageing in place was the dominating plan, although one quarter of the respondents answered that they did not know what would happen in the future. Those who planned to move wanted to move to housing that required less maintenance and to a more central location. Residential mobility is at play also in old age as 27% of the respondents had moved at some point during the last 10 years, i.e., after the age of 70.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.204
Threshold uncertainty score0.856

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.012
GPT teacher head0.243
Teacher spread0.231 · 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

Citations10
Published2019
Admission routes1
Has abstractyes

Explore more

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