Gender Differences in Language Acculturation Predict Marital Satisfaction: A Dyadic Analysis of Russian-Speaking Immigrant Couples in the United States
Bibliographic record
Abstract
Immigration is a major life event that can create marital distress in couples who migrate together (Min, 2001 ). One instigator of this distress is differences in the rates of acculturation between husbands and wives (Chun and Akutsu, 2003 ; Darvishpour, 2002). Previous qualitative research (Ben-David and Lavee 1994) found that amongst Russian immigrant couples in Israel, marital discord resulted when wives acculturated faster to the host culture than their husbands. Building on this with a quantitative approach, we assessed a sample of Russian-speaking immigrants in the United States of America to examine whether gender differences in acculturation patterns predicted marital satisfaction. Fifty immigrant couples completed the Language, Identity and Behavior Acculturation Scale (Birman and Trickett, 2001) and the Revised Dyadic Adjustment Scale (Busby, Christensen, Crane, and Larson , 1995) as an indicator of marital satisfaction. Multilevel modeling revealed that husbands and wives were less satisfied in their marriages when husbands scored low on American language acculturation and wives scored higher. Differences in the other dimensions of acculturation (American and Russian identity and behavior) did not significantly predict marital satisfaction. To interpret these findings, we suggest that language acculturation is a pathway to economic success, and when women acculturate faster it violates traditional economic gender roles. Implications for therapists and community agencies who support immigrant families during this difficult transition are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| 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".