What Are Friends for in Russia Versus Canada?: An Approach for Documenting Cross-Cultural Differences
Bibliographic record
Abstract
Most research on friendship has been grounded in Western cultural worlds, a bias that needs to be addressed. To that end, we propose a methodological roadmap to translate linguistic/anthropological work into quantitative psychological cross-cultural investigations of friendship, and showcase its implementation in Russia and Canada. Adopting an intersubjective perspective on culture, we assessed cultural models of friendship in three inter-related ways: by (1) deriving people’s mental maps of close interpersonal relationships; (2) examining the factor structure of friendship; and (3) predicting cultural group membership from a given person’s friendship model. Two studies of Russians (Study 1, n = 89; Study 2a, n = 195; Study 2b, n = 232) and Canadians (Study 1, n = 89; Study 2a, n = 164; Study 2b, n = 199) implemented this approach. The notions of trust and help in adversity emerged as defining features of friendship in Russia but were less clearly present in Canada. Different friendship models seem to be prevalent in these two cultural worlds. The roadmap described in the current research documents these varying intersubjective representations, showcasing an approach that is portable across contexts (rather than limited to a specific cross-cultural contrast) and relies on well-established methods (i.e., easily accessible in many research contexts).
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".