A Systemic Functional Linguistics Analysis of Text Transitivity of Mathew Gospel, New Testament of Kupang Malay
Bibliographic record
Abstract
This article was aimed to describe text transitivity of Mathew, New Testament of Kupang Malay language called Janji Baru. Data were taken from the Bible, New Testament, especially in Mathew Gospel which consisted of 29 sections/chapters. All data were analyzed qualitatively and the analysis was based on systemic functional linguistic theory focused on transitivity system. The result showed that transitivity in texts consisted of three elements. (1) Participants were realized by nominal group, including personal pronouns that consisted of: listener only that was realized by lu ‘you’; speaker + listener: ‘katong, ’we’; speaker only: beta ’I’; speaker + others: botong ‘we’; listener + others: ’bosong ‘you’; others: ‘dong’ ‘they’; someone/ something, conscious/non- conscious: ‘dia’ ‘he/she.it’; general: orang dong ‘people’. (2) Circumstances that were realized by time, location, reason, cause and manner, such as, sakarang ‘now’, ‘nanti’ next time’, ‘beso lusa’ ‘tomorrow;; locations, kampong ’village’., ‘dano ‘lake, sabla ‘beside’ ; tagal ‘because’,etc (3) Processes that was realized by verb groups containing six types: material process as pi datang ‘go’ pi ambil ‘take’, `lempar buang ‘throw’, potong buang ‘cut’, jalan pikol bawa ‘carry’; verbal as, kasi tau ‘tell’, angka janji ‘promise’ ; ’relational as jadi ‘become’; existence as ada exist; mental as tau ‘know’, and behavioural as maruak ame, ‘get angry’, lia pi ‘see or watch’, parcaya ame ‘believe’. Almost all process excepted existence and relational combined two or three verbs for one meaning. This fact indicates to let readers or listeners understand the message.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".