Acquisition of prenominal adjective order by Jordanian EFL learners
Bibliographic record
Abstract
This study investigates how Jordanian EFL learners manage to learn the order of English prenominal adjectives, shedding light on learners' cognitive processes and the possible impact of their first language. It focuses on two-, three- and four-adjective sequences to identify the areas of difficulty and their sources. The authors of the present study relied on their experience. They referred to some experts in Arabic grammar to compare the students' order of English prenominal adjectives with the order of Arabic adjectives to inform the degree of their mother tongue's influence. A test based on the order of prenominal adjectives suggested by Svatko (1979) was used for data collection to achieve the study objectives. The study participants were 42 Jordanian advanced EFL undergraduate students at Al-Hussein Bin Talal University in Jordan. The study results revealed that Jordanian EFL learners encounter great difficulties in using prenominal adjectives, especially as the complexity of sequences increases. The overall percentage of correct answers across all categories is 35%. The results also showed that intralingual errors outweighed interlingual errors, scoring 77%.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".