A cross-cultural study of condolence strategies in a computer-mediated social network
Bibliographic record
Abstract
Among the various speech acts, an under-investigated one is condolence speech act. The present study sought to investigate the verbal strategies of expressing condolence used by (1) Iranian native speakers of Persian, (2) Iranian EFL (English as a Foreign Language) learners, and (3) American native speakers of English. Accordingly, a total of 200, 42, and 50 responses were collected respectively from the informants who responded to an obituary post followed by a picture consisting of a situation related to the news of a celebritys death on Instagram (In the case of Iranians: Morteza Pashaii , a famous singer in the case of Americans: B. B. King , an American singer-songwriter). After creating a pool of responses to the death announcements and through careful content analysis, the utterances by native Persian speakers, EFL learners, and native English speakers were coded into seven, nine, and seven categories, with expression of affection ( n = 109, 46.38%), wishes for the deceased ( n = 34, 59.64%), and wishes for the deceased ( n = 32, 23.70%) being the most prevalent ones, correspondingly. Moreover, tests of Chi-square revealed that there was a statistically significant difference among the three groups. The results showed that there were significant differences among the participants in terms of using condolence strategies in Expression of affection (love and grief), Wishes for the deceased, Expression of shock, use of address terms, expression of gratitude, Offering condolences, expression of happiness for his peaceful death, and Seeking absolution from God categories, with Expression of affection being the most prevalent one among Iranian Persian speakers. The findings have pedagogical implications for EFL teachers as wells as textbook and course designers.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".