Iraqi EFL University Students’ Linguistic Strategies in Approaching Warning and Prohibition
Bibliographic record
Abstract
This paper aims at detecting Iraqi EFL students’ ability in recognizing and producing the speech acts of warning and prohibition, finding the reasons behind their failure and attempting to find possible solutions. The data are responses of 60 fourth year Iraqi EFL college students who participated to answer a two-part test of recognition and production. It is hypothesized that the Iraqi EFL learners commit more errors in producing SAs of warning and prohibition than recognizing these two SAs, they find more difficulty in handling the SA of prohibition than of warning, they show a tendency towards using a particular strategy to express the SAs of warning and prohibition, and they also confuse the SAs of warning and prohibition with other relevant SAs at the recognition and production levels. The paper concludes that Iraqi EFL learners recognize warning expressions better than those conveying prohibition. They also show sufficient awareness in issuing warning strategies in various situations. By contrast, they display a poor awareness of using various strategies in performing the SA of prohibition. Furtherly, the students’ level in recognizing the SAs of warning and prohibition is higher than their level in producing these SAs. Correspondingly, in producing the SAs of warning, Iraqi EFL learners show a higher preference for using negative imperative, modals and if-conditional strategies more than other types in most of the situations, whereas in communicating the SA of prohibition, they employ negative imperatives and modals more than any other construction.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".