Bibliographic record
Abstract
This article addresses two issues regarding Israeli emigrants. First, it focuses on their number and distribution in various destination countries; second, it deals with patterns of self-selection among emigrants, namely, the skill level of Israelis who select themselves to leave Israel for various destination countries. The findings suggest that Israeli emigration has increased in the past two decades, but that most of the increase was in the 1990s, and was due to the emigration of foreign-born Israelis, rather than the emigration of native-born Israelis. Based on the DIOC (Database on immigrants in OECD countries) about 164,000 Israeli-born emigrants, aged 15 years and over, resided in 25 OECD countries in 2000, suggesting that relative to other countries, the share of Israeli-born residing outside Israel is not high. Two-thirds of Israeli-born emigrants were in the US, and 85 percent in the Anglo-Saxon countries. The selectivity of Israeli emigrants, measured by education and occupation, is most positive in the Anglo-Saxon countries, especially the US, where the returns on skills are the highest. By contrast, the least skilled Israeli emigrants choose Scandinavian countries, where the labor markets are relatively rigid, and returns on skills tend to be the lowest. These findings are consistent with migration selectivity theory, which anticipates that high-skilled immigrants will choose destinations where their skills will be generously compensated. Finally, the results suggests that the educational selectivity of Israeli emigrants to the Anglo-Saxon countries (but not to Scandinavian countries) has improved in the late 1990s compared to the early 1990s.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".