Literary Enchantment: Unraveling 'Laskar Pelangi' through Transitivity and Personification Clauses in Higher Education Literature Studies
Bibliographic record
Abstract
This research delves into the analysis of personification clauses within the Indonesian popular novel "Laskar Pelangi" by Andrea Hirata. Utilizing Halliday and Matthiesen’s (2014) transitivity framework, the study employs Spradley’s (2016) analytical phases: domain, taxonomy, componential analysis, and finding cultural value. The findings reveal seven experiential processes in the transitivity of personification clauses, including material process, relational attributive process, verbal process, mental process, mental behavioural process, verbal behavioural process, and relational identifying process. Material and mental processes dominate in simplex clauses, while in complex clauses, the dominant processes include mental process, mental behavioural process, attributive relational process, material process, verbal process, and verbal behavioural process. The dominant occurrences of the participants and processes reflect the use of “personified objects” in that the inanimate can do, talk, move, think and feel like the animate. Thus, this empirical investigation showcases the novelist's inventiveness and vivid imagination in describing the novel's events. This research demonstrates the critical role of imagination and creativity in novel writing. The more innovative and original the work, the more enjoyable it is for readers to read and appreciate. Pedagogically, the findings from the study are associated with cultural values and morality. The cultural values embedded in the Indonesian popular novel "Laskar Pelangi" can serve as a catalyst for teaching literature in higher education contexts.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.001 | 0.004 |
| 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".