Eliciting Persian Requests: DCT and Role Play Data
Bibliographic record
Abstract
The validity of speech act data taken from different kinds of elicitation instruments has been widely discussed in interlangauge and cross cultural pragmatics literature. In this study an attempt is made to evaluate and investigate data taken from two of the most popular speech act instruments namely, written DCT and closed role play. The responses of forty Iranian university students in their native language (Persian) were investigated across the speech act of request. The distinguishing feature of the study is using the same participants for the two methods; in this way we can have an account of intra-participant variations. The social status and distance of the participants were both equal. Differences were found in the length and content of the responses. Respondents to role play tended to have longer responses and it was mainly because of the longer and higher number of alerters and supportive moves used in the role play. In the written data we witnessed more direct strategies used in the request Head Acts. Likewise, modification devices used in the oral data had a softer tone and in terms of request perspective the oral data provided more impersonal responses while the requests in the written data were more hearer-oriented. Overall, it seems that the data gathered through role play is more natural than DCT. Finally it is discussed that the choice of data gathering instrument in pragmatics research is to a large extent dependent on the goals of the researcher and research questions.
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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 | 0.008 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".