Bibliographic record
Abstract
The aim of this study was an attempt to investigate the use of conjunctions in argumentative essays written by English as a Foreign Language fourth-year undergraduate Libyan students majoring in English at Omar Al-Mukhtar University in Libya. A corpus of 32 argumentative essays was collected from a sample of 16 students in order to be investigated in terms of Halliday and Hassan’s (1976) taxonomy of conjunction. Findings showed that the Libyan EFL students used the conjunctions inappropriately, and that the adversative conjunctions posed the most difficulty for the learners, followed by additives and causals. Of the adversatives, on the otherhand was the most difficult conjunction for the participants, followed by but and in fact. With the use of additive conjunctions, moreover was the most problematic, followed by andandfurthermore. Among the causals, the conjunction so was the most challenging, followed by because. The findings of this study confirm previous studies that learners of English as a foreign language have difficulty in using conjunctions in their writing. The difficulties encountered by participants in employing the conjunctions can be attributed to three reasons: 1) first language (Arabic) negative transfer; 2) overgeneralisation in the second language (English) and 3) the presentation of conjunctions in lists in ESL/EFL textbooks without showing the subtle difference between them in terms of semantic function. These findings are discussed in this paper with implications for teaching the use of conjunctions in the Libyan context.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".