Dialogue Model, Conflict, and Context in Drama Text Works by Arifin C. Noer
Bibliographic record
Abstract
Dialogue, conflict, and context are paramount in the drama text. The research of these three things can reveal the meaning, aesthetics, and ideology that blend in the drama text. Drama text research has not obtained comprehensive results if it has not revealed all three. The purpose of this research is to find the dialogue model, conflict, and context in drama text by Arifin C. Noer. The approach used in this research is the critical discourse of Teun A. Van Dijk. Data are analyzed in three dimensions, namely text, social cognition, and social context through macro structures, superstructures, and microstructures. The global macro structure is reflected in the synopsis, the superstructure is seen from its builder elements, and the microstructure contains the use of language. The results of the microstructure research found that the dialogue can be configured in several models, i.e. by topic, principles of cooperation, principles of politeness, speech acts, and speech series. The conflict was created with a model of pragmatic, socio-psychology, and ideological principles use. The context model is the use of physical, epistemic, linguistic, and social context. The results of this research contribute to the increased appreciation of drama and reference texts in its teaching.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".