Acculturation Process of Arab-Muslim Immigrants in the United States
Bibliographic record
Abstract
While globalization has made this world smaller, interdependent, and heterogeneous, clashes among different cultures became inevitable. Immigrants leave their home country for many reasons, by choice or necessity. The U.S. is considered one of the countries that enjoys its cultural diversity. In the case of Arab-Muslim immigrants, they came to the U.S. either seeking a better life, or fleeing prosecution. They come from completely different culture, language, and religion. This move makes them prone to experience one or more challenges: assimilation, integration, separation, or marginalization. Since assimilation is very hard to achieve, integration is the ideal choice for which scholars aspier. This paper investigates the acculturation process of Arab-Muslim immigrants in the U.S. Results showed a variety of potential barriers exist hindering Arab-Muslim immigrants from successful integration into the United States society. Cultural and religious differences, distinctions in moral and ethical values, perception of gender relations, demonization of the Arab population in mass media, and discrimination are the major factors causing the overall struggles of the acculturation process.
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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 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 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".