Bibliographic record
Abstract
Cross-cultural psychology attempts to understand the development and expression of human behavior in relation to the cultural contexts in which it occurs. It adopts the perspective of "universalism," which assumes that all human beings share basic psychological processes, but which are then shaped by cultural influences. This perspective allows for the comparison of individuals from different cultures (based on the process commonality), but also accepts behavioral variability (based on the cultural shaping). In the case of behavior that takes place during interactions between individuals coming from two (or more) cultures, the task is more complex; we now need to understand at least two sets of culture-behavior phenomena, as well as a third set--those that arise at the intersection of their relationships. In cross-cultural psychology, we have adopted concepts and methods from sociology and political science to inform work on "ethnic relations," and from cultural anthropology we have been informed in our work on the process and outcomes of "acculturation." In the former domain are phenomena such as prejudice and discrimination; in the latter are the strategies people use when in daily contact with people from other cultures (such as assimilation, integration, separation, and marginalization). These phenomena take place in cultural contexts, which need to be understood in terms of the core dimensions of cultural difference (such as diversity, equality, and conformity). During prolonged and intimate contact between persons of different cultural backgrounds, all these psychological concepts and processes, and cultural influences need to be taken into account when selecting, training, and monitoring individuals during their intercultural interactions.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.028 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".