Systemic functional linguistics-legal genres and their configurations in the Islamic law and jurisprudence textbooks at a university in Indonesia
Bibliographic record
Abstract
The great importance of textbooks in the English language in the academic, pedagogic, and scientific world is uncontested. Acquiring holistic knowledge of legal transdisciplinary is of great importance to Islamic law students in Indonesia. Nevertheless, English reading proficiency of Indonesian students is problematic. The present research was to identify the genre types and unfold them through what patterns the genres are mostly structured. Data of the study were one Islamic Law textbook and one Jurisprudence textbook used as teaching resources and required reading at Universitas Islam Negeri Sumatera Utara, Indonesia. Based on the five main Systemic Functional Linguistics-based genre frameworks for the analysis, findings from the Islamic Law textbook showed 18 genre types including three proposed ones under four genre families of which History genres are the most frequent ones followed by Explanation, Report, and Argument genres. On the other hand, 16 genre types including three new ones belonging to four genre families were identified in the Jurisprudence in which Report genres are the most frequent ones followed by Argument, Explanation, and History genres. The commonalities and discrepancies of the findings between the two legal textbooks are assumed to be the logical results of the ideological differences and the resource aspects from which the legal discipline is oriented. The findings of the study would be useful to design teaching of reading legal English texts that can facilitate students which is unfortunately neglected by both English and Law teachers.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| 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".