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Record W3171580706 · doi:10.1177/10693971211024599

What Are Friends for in Russia Versus Canada?: An Approach for Documenting Cross-Cultural Differences

2021· article· en· W3171580706 on OpenAlexaffabout
Marina M. Doucerain, Andrew G. Ryder, Catherine E. Amiot

Bibliographic record

VenueCross-Cultural Research · 2021
Typearticle
Languageen
FieldPsychology
TopicCultural Differences and Values
Canadian institutionsConcordia UniversityJewish General HospitalUniversité du Québec à Montréal
Fundersnot available
KeywordsFriendshipPerspective (graphical)Social psychologyInterpersonal relationshipInterpersonal communicationCross-culturalSociologyCultural diversityPsychologyAnthropologyComputer science

Abstract

fetched live from OpenAlex

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).

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.120
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0060.003
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.351
GPT teacher head0.548
Teacher spread0.197 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2021
Admission routes2
Has abstractyes

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