Long Distance Commuting and Couple Satisfaction in Israel and United States: An Exploratory Study
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".