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Record W2410613604 · doi:10.3138/jcfs.44.6.765

Long Distance Commuting and Couple Satisfaction in Israel and United States: An Exploratory Study

2013· article· en· W2410613604 on OpenAlexvenueno aff
Judy Landesman, Rudy Ray Seward

Bibliographic record

VenueJournal of Comparative Family Studies · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsnot available
Fundersnot available
KeywordsPsychological resiliencePsychologySocial psychologyDemographic economicsExploratory researchSociologyEconomics

Abstract

fetched live from OpenAlex

Employment related mobility is on the increase and long distance extended commuting has become a substitute for migration. Details on patterns of voluntary long distance extended mobility were gathered and propositions based on deficit and resilience perspectives of the impact of commuting couple satisfaction were assessed. From 2010 to 2012, using a staticgroup comparison research design both quantitative and qualitative data were gathered from a non-probability convenience sample of 434 respondents in Israel and 130 in the U.S. Commuters, especially in Israel, were most often males, older, married and in relationships longer than non-commuters. While length of commuting did not impact overall satisfaction with commuting couples' relationships, frequency of commutes had a modest negative impact. While satisfaction reports from noncommuting couple members were consistently higher than the reports from commuting couples, the majority of both groups reported satisfaction with their relationships. But commuting appears to have a modest negative impact on couples' satisfaction with their relationships. Women who supported gender specific roles for couples were more likely than men to report greater satisfaction with their relationships. Data limitations identity obstacles to overcome and need to further assess potential risk and protective factors, especially resilience.

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 score0.299
Threshold uncertainty score0.949

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 communication0.0000.001
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 head0.115
GPT teacher head0.391
Teacher spread0.275 · 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

Citations11
Published2013
Admission routes1
Has abstractyes

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