Social capital, acculturation attitudes, and sociocultural adaptation of migrants from central Asian republics and South Korea in Russia
Bibliographic record
Abstract
This research examines the relationship of social capital with the acculturation attitudes and sociocultural adaptation of 122 migrants from Central Asian republics of the former Union of Soviet Socialist Republics (Uzbekistan, Tajikistan, Turkmenistan, Kyrgyzstan, and Kazakhstan) and 136 migrants from South Korea. The questionnaire included scales for assessing acculturation attitudes (integration, assimilation, and separation), individual social capital (bridging and bonding), and sociocultural adaptation. Using parallel mediation analysis, we found that acculturation attitudes for migrants from Central Asia are secondary to their social capital in relation to sociocultural adaptation. However, among migrants from South Korea, social capital is not linked to their acculturation attitudes, and in general, its role in sociocultural adaptation is lower as compared to the role of acculturation attitudes. As a whole, our research shows that although sociocultural adaptation for all ethnic groups is linked to acculturation attitudes and social capital, acculturation attitudes for certain ethnic groups can be dependent on social capital.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".