Thematic Progression Pattern in Al-Hikam Aphorism Arabic – Bahasa Indonesia and Arabic – English; Systemic Functional Linguistic Approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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