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Record W982620210 · doi:10.2747/0272-3638.7.6.497

MOBILITY INTENTION AND SUBSEQUENT RELOCATION

2013· article· en· W982620210 on OpenAlexaffabout
Eric G. Moore

Bibliographic record

VenueUrban Geography · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsQueen's University
Fundersnot available
KeywordsRelocationDisadvantagedConceptualizationSpousePsychologySample (material)Demographic economicsSociologyQuality of life (healthcare)Social psychologyGerontologyEconomic growthEconomicsMedicine

Abstract

fetched live from OpenAlex

Conceptualization and analysis of the relations between dwelling and neighborhood satisfaction, movement intention, and mobility presented in previous research suffer from two major deficiencies: (1) the role of other life events such as marriage, divorce, retirement, and loss of spouse are usually ignored; (2) important differences between different sociodemographic groups are obscured. In this paper a more general model of mobility including other life events is presented and the subgroup differences are explored in terms of the difficulties experienced by disadvantaged groups, particularly the elderly, in translating expectations into action. Two sets of longitudinal data are utilized, one from the Community Development Strategies Evaluation undertaken in nine U.S. cities and the other from the Quality of Life Surveys collected by the Institute of Behavioural Research at York University for a Canadian sample. Consistent results are obtained which show that not only do substantial numbers of respondents...

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.013
GPT teacher head0.253
Teacher spread0.240 · 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 source (direct Gemma or distilled Codex), 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

Citations28
Published2013
Admission routes2
Has abstractyes

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