Thematic Progression Pattern in Al-Hikam Aphorism Arabic – Bahasa Indonesia and Arabic – English; Systemic Functional Linguistic Approach
Bibliographic record
Abstract
This research investigated the information structure in translated texts Arabic – Bahasa Indonesia and Arabic – English, and how the structure is developed in terms of thematic progression pattern, so the text can be cohesive. This study also examines whether there is a topic change from the source language (SL) to the target language (TL). The method used in this study was divided into three phases: data collection, data analysis, and research report. The total of data used in this research were 435 clauses with thematic structure from 100 aphorisms in al-Hikam aphorisms Arab – Bahasa Indonesia and Arabic – English. The high percentage of unmarked topical theme shows that, textually, the information distribution in the aphorisms Arabic – Bahasa Indonesia and Arabic – English is organized in a coherent and systematic way. There are 64.35% of unmarked topical theme in Arabic – Bahasa Indonesia, and there are 59.62% in Arabic – English. The linear and zig zag progression patterns do not experience shift. Meanwhile, there is a shift in the multiple and distributed patterns. This has an impact on the level of cohesion and wholeness of the message in the thematic structure of al-Hikam aphorisms. Contextually, this research contributes to the study of cross-language and cross-cultural. A translator must be more careful in translating aphorisms in both Arabic – Bahasa Indonesia and Arabic – English since the progression patterns are multiple and distributed. Based on these results, it can be concluded that Theme mapping in information structure is an important thing that a translator should pay attention to.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".