An episodic framework of outgroup interaction processing: Integration and redirection for the expatriate adjustment research.
Bibliographic record
Abstract
Cross-cultural research has traditionally emphasized predicting adjustment, treating it as a level to be achieved more than a change process to be understood and controlled. The lack of focus on process integration has inhibited our understanding of precisely why and how adjustment processes unfold and ultimately cause (dys)functional change in criteria. In response, we review the motives and processes of cross-cultural adjustment and integrate these into a theoretical framework, examining the discrete episode of expatriate-host national interaction as the focal vehicle for change. First, we synthesize the general causal sequence within an interaction episode. We then summarize state inputs that condition processing. Next, we describe identity management and learning processing in depth. Then, we discuss key interactions among the motive and processing categories. Finally, we orient the cross-cultural interaction episode within the nomological network of cross-cultural adjustment predictors and criteria. This framework prescribes that an expatriate should initially reduce acculturative stress through repeated, functional identity management and learning processing of novelty encountered in cross-cultural interaction episodes. To do so, one must avoid inhibitory input states and the many potential processing failures identified here. If the expatriate experiences enough such functional interaction episodes, a "Stage 2" is reached where the motive to reduce stress has been largely overcome, and thereafter, interaction episode processing proceeds more functionally in general. (PsycINFO Database Record
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".