From Theory to Practice: A Contrastive Analysis of English and Indonesian Noun Clauses for Educators and Learners
Bibliographic record
Abstract
This study conducts a contrastive analysis of noun clauses in English and Indonesian, focusing on their structural and functional characteristics. Given the fact that both of them have different verbal patterns, it is interesting enough to have a look at their nominal clauses, which should be one of the linguistic focuses based on their functional relationship towards the major elements involved in the process. Using a qualitative descriptive method, the research examined noun clauses from English and Indonesian context. The analysis revealed significant similarities in the functionality and positioning of noun clauses in both languages, serving as subjects, objects, objects of prepositions, and subjective complements. Both languages employ specific connector words to introduce noun clauses, although with some variations in usage. Key differences emerge in the contextual usage of connector words and the explicitness of prepositions, with Indonesian tending towards more direct expressions and explicit preposition use compared to English. These findings contribute to a deeper understanding of the linguistic structures in both languages and have implications for language teaching and learning. It is recommended that learners avoid using easily accessed helping verbs only in English noun clauses. For more emphasis, learners should also practice using modal sentences and modals themselves. A suggestion to avoid critical examination times of tenses and moods, and singular and plural noun forms, especially for Bahasa-speaking learners, should be facilitated by more language exposure. The Indonesian language has no relative pronouns, and almost all that is required are prepositional pronouns. With respect to this feature, the insertion of conjunctions and the use of 'that' can be good alternatives. Generally, fewer errors are made by inserting conjunctions, but they could also be indicated.
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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 | 0.018 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".