Mood analysis on English script drama of Peterpan story by J.M Barrie
Bibliographic record
Abstract
This study was conducted to describe and explain the process types of mood analysis that are characterized in the English script drama of Peter Pan story. This study revealed the dominant process type in the scripted drama and also aims to explain the pedagogical implications of the mood analysis. The object of this study is the English script drama of Peter pan story. This study concerned functional grammar, mainly on mood analysis. Some theories relate to the literature, discourse, grammar, drama script and interpersonal meaning itself are used in order to get the objective of this study. The technique of data analysis in this study is a qualitative one. The procedure of collecting data included reading, selecting, identifying, classifying, and interpreting the data. In analyzing, the data are collected by reading, identifying, and classifying them into clauses. The mood analysis is conducted to figure out the type of process in all of the clauses, and then explained each type of process found in the scripted drama. The argumentation is also given to support the comparison between the theories and the analysis. The study found that there are four types of dominant mood found in the scripted drama. The declarative mood is found 80%, the interrogative mood is found 14%, the imperative mood is found 4% and the exclamative mood is found 1%. From the result, it can be concluded that in composing script drama uses a more declarative mood.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".