Intercultural Relationships: Entry, Adjustment, and Cultural Negotiations
Bibliographic record
Abstract
Globalization, technological advances, and increasingly flexible social norms have contributed to more widespread intercultural relationships, particularly in multicultural societies such as the United States. In this paper, the authors use Bronfenbrenner’s ecological systems framework (i.e., the macrosystem, exosystem, microsystem, chronosystem) to review the factors involved with entry and adjustment in intercultural relationships. Relationship entry is discussed in terms of how people of different cultural backgrounds meet, interact, and intimately relate. Factors that impact the likelihood of entering an intercultural relationship are outlined. Adjustment refers to the manner in which partners cope with the dyadic tensions that impact their relationship satisfaction and functioning. Compared to intracultural couples, intercultural couples are at a higher risk of experiencing adjustment problems over the course of the relationship. Therefore, suggestions are provided to help these couples minimize conflict and optimize satisfaction. Factors relating to relationship identity and cultural negotiation also are discussed. A summary of the most important clinical ideas to emerge from the literature on intercultural couples in the United States, as well as clinical suggestions for therapists working with these couples, are provided at the end of the paper.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".