Work Relations Between Immigrants and Old-Timers in an Israeli Organization: Social Interactions and Inter-Group Attitudes
Bibliographic record
Abstract
Although immigrant workers have become an integral part of most organizations in immigrant-receiving countries, there is surprisingly little research on cross-cultural work relations, especially in the professional/white-collar sector. In Israel, where former Soviet immigrants comprise over 20 percent of the Jewish population, the presence of Russian-speaking workers and professionals is dense in almost every workplace.The current qualitative study is focused on everyday work relations and inter-group attitudes between long-time Israeli residents and recent Russian-speaking immigrants in the context of a medical organization. Twenty-five interviews with the veteran and new immigrant workers (conducted in Hebrew and in Russian) indicate that these groups diligently guard their social borders and separate identities, and share similar critical opinions on each other’s work ethic and competence. Conflicts arise around the issues of educational and work status gaps, relations with supervisors, and language use. The continuing use of Russian by immigrant workers is interpreted by Hebrew-speakers as a sign of their separatism and anti-Israeli outlook. The underlying mechanisms of mutual stereotyping are discussed.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 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".