Maritime Students Meeting the Maritime Industry English Standards: An Analysis of Types of Sentences
Why this work is in the frame
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Bibliographic record
Abstract
A sentence is the highest unit of grammar. Thus, constructing error-free sentences in writing is one of the biggest challenges encounted by most non-native speakers, and even university students are not an exception to this reality. This study aims at investigating various types of sentences produced by tertiary-level maritime students in a Sri Lankan university. The study was based on a narrative writing activity in the English for Academic Purposes (EAP) module. The students were provided forty-five minutes to produce the piece of writing as an in-class activity on a topic relevant to their field visit to a port. This is a descriptive study based on the analysis of a small corpus of essays written by twenty maritime students, and a structural analysis of sentences was employed to examine the students’ writing. Different kinds of sentences and sentence errors were identified, and they were classified accordingly. The findings of the study revealed that the students favoured simple sentences over other sentence types. Approximately two-thirds of the sentences produced belonged to the simple sentence category. The compound-complex form was found to be the least utilized sentence type among the target group. The analysis of sentences was based on the elements of the clause structure explained in Quirk et al. (1985) and Oshima and Hough (2006). Interestingly, it was observed that there was no single common clause structural pattern used by the participants. Instead, they used subject-verb-object (SVO), subject-verb-complement (SVC) and subject-verb-adverbial (SVA) types very often when writing. Similarly, fragments and run-on sentences were recorded high among maritime learners’ erroneous sentences in writing. The study findings have pedagogical implications for the teaching of English language grammar that subsumes essay writing in the EAP module. 
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it