Why we move to Israel? To integrate into Israeli society or “get more bang from the buck”
Bibliographic record
Abstract
What makes lifestyle migrants (individuals from wealthy countries permanently relocating to another wealthy country) satisfied with their move to Israel? Some believe lifestyle migrants are most satisfied when they reside in well-to-do, cultural oases, settled by like-minded migrants. Others report the opposite – that lifestyle migrants who move to Israel are most satisfied when they integrate into Israeli society. This study asks which of these two approaches led to life satisfaction for English-speaking, lifestyle migrants? Using a cross-sectional study, working-age, Jewish adults (n = 109) who recently emigrated from Canada, the United Kingdom and the United States were recruited. We found that life satisfaction post-migration in Israel was highest among lifestyle migrants who achieved their pre-migration desire to integrate into the host country’s social-, cultural- and work-life. No evidence supported the contention that lifestyle migrants moved to Israel with the desire to live in well-to-do, cultural, enclaves. In fact, contrary to many studies, pre-migration and post-migration levels of socioeconomic status were unrelated to lifestyle migrants’ life satisfaction. While privileged, Jewish migrants may arrive to Israel with a higher level of socioeconomic status than others, they strive for their pre-migration goal, to become part of Israeli society.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".