Age and gender in Iranian ‘<i>taaroff</i>’ politeness system
Bibliographic record
Abstract
Abstract This paper focuses on the Iranian taaroff politeness system. We report a quantitative analysis of the attitudes to taaroff held by 60 Iranians (30 women and 30 men) of two age groups (20–29 and 40–59 years old) and their use of formulaic taaroff expressions in conversations. The data come from dialogues elicited from the participants in Iran via short scripted scenarios and from their answers to a questionnaire survey about their attitudes to taaroff. Taaroff expressions were manually extracted from the dialogue transcripts and their overall use as well as frequencies of each expression were compared across the gender and age groups with the help of t-tests. The participants’ answers to the survey questions were compared across the groups with Kruskal-Wallis H tests. The results show statistically significant differences in the participants’ attitudes to taaroff and in its use in dialogues by gender and age group.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".