16Copyright © Canadian Academy of Oriental and Occidental Culture Request Strategies by Second Language Learners of English: Pre- and Post- Head Act Strategies
Bibliographic record
Abstract
This study investigates the speech act of request by Saudi high- and low-level learners of Australian English. All participants were asked to take part in three different role plays, which varied according to the relative power relationship between the informant and the conductor. We found that high-level learners did not considerably differ from low-level learners in terms of pre- and post-head act strategies, and request strategies; thereby indicating that proficiency level does not have a significant impact on L2 learners ’ choice of pre- and post-head act strategies and request strategies. However, both groups of learners deviated from Australian English native speakers in terms of post-head act and request strategies. In light of the social variable (power) influence, it was found that power affected both groups of learners, along with the native speaking group, in terms of pre- and post-head act strategies. However, power did not have an impact on the SLL group, while it did have an effect on the high-level group, along with the native speaking group, in terms of request strategies. Thus, there is no apparent correlation between the social variable (power) and L2 learners’ use of pre- and post-head act strategies, while power positively correlates with L2 learners ’ proficiency level regarding their use of request strategies.
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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 | 0.001 | 0.002 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.030 | 0.003 |
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".