Being a droog vs. being a friend: A qualitative investigation of friendship models in Russia vs. Canada
Bibliographic record
Abstract
Introduction. A substantial body of work has established that friendship is an important non-kin interpersonal relationship, with many positive outcomes. An issue with this literature is that it originated primarily in anglocentric Euro-American societies, when several studies have shown that the meaning of friendship varies across cultural settings. In particular, linguistic analyses advance that the meaning of friendship in Russian is quite different from that in English. The goal of this study was to seek psychological evidence of these linguistic findings by documenting similarities and differences in people’s understanding of friendship in both cultural contexts. Methods. The research consisted of a qualitative investigation of friendship cultural models among Russian migrants to Canada, through semi-structured interviews that were analysed using an inductive thematic analysis, whereby data segments are coded and codes are gradually refined and streamlined in order to identify the main themes that emerge from the data. Results. Participants’ depictions of friendship in Russian vs. Canadian contexts were largely in line with semantic analyses of friendship in Russian vs. English, with friendship being described as a stronger and deeper bond, but also more demanding in Russia than in Canada. Discussion. The findings support Wierzbicka’s proposal that key terms in a language encapsulate cultural models prevalent among its speakers. The results are also consistent with the existence of close parallels between people’s cultural models and the linguistic ecologies in which they live.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.024 | 0.016 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".