A Comparative Study of Request Speech Acts in Badrudi, Persian, and English
Bibliographic record
Abstract
This study was an attempt to delve into how requestive speech acts were realized in English, Persian, and Badrudi by EFL Persian students at the upper-intermediate level who spoke Badrudi as well. To collect the required data three versions of a Discourse Completion Test in English, Persian and Badrudi were administered to the research sample (N=40). The collected data was codified and analyzed with regard to social power and distance as two contextual variables and further statistical procedure was run to sustain or reject the research hypotheses. The analysis of the data revealed that the presence of power in interactions influences the request realization by the participants as there were differences among the most frequent strategies in English, Persian, and Badrudi. Moreover, with respect to the presence of social distance the comparison among request strategies in English, Persian, and Badrudi indicated the participants opt for different strategies as the most frequent strategies. The results also revealed that with the presence of power and distance direct strategies were the most frequent ones, conventionally indirect ranked second and indirect strategies were used as the least frequent strategies in English, Persian, and Badrudi.
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 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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".