Bibliographic record
Abstract
Due to long academic experience, it was noticed when some EFL learners encounter a problem in verbal communication in TL, they tend to employ different techniques. They may abandon the message, alter the meaning they intend to convey, omit some items of information, make their ideas simpler and less precise, or say something which is slightly different from the intended meaning. When a learner is able to anticipate such a communication problem, he may overcome it by avoiding communication or modifying what he intends to say. If the problem arises while the learner is already engaged in speaking, he may try to find an alternative way of getting the meaning across. The researcher witnessed various types of communication strategies used by learners in their interaction and performing tasks via English. This actual observation motivated the researcher to investigate the communication strategies (CSs) employed by EFL learners in communicating with others, e.g. their classmates and instructors. This observation is also in line with what other researchers (e.g., Littlewood, 1984; Poulisse, 1987) have noticed that EFL learners who venture to put their knowledge into practice often run into communication problems due to deficiencies in their linguistic repertoire. The present study deals with CSs and the proficiency level of 66 Jordanian students at Zarka University. Key words: Jordanian learners; Communication strategies; Strategic competence
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".