Constituent Structure of the Simple Sentences Found in Peter Pan Novel
Bibliographic record
Abstract
The study aims to analyse the constituent structure of the simple sentences which were constructed in the “Peter Pan” novel. This research used descriptive qualitative method. The steps in taking and collecting the data were reading the novel, underlining the simple sentences, and analysis the constituent structures. The theory of Brown and Miller (1991) to represented the tree diagram and constituent structures. And used the theory of Quirk et al (1973) to support the analysis. Based on the results, this research found that there were 182 data of simple sentences which were used in the “Peter Pan” novel by J. M. Barrie. The sentences which were found divided into two branches, those are Noun Phrase (NP) and Verb Phrase (VP). Most of the Noun Phrase (NP) that was found in the sentences have the daughter of Determiner, Noun and Pronoun. Meanwhile, the Verb Phrase have the daughters, there were Auxiliary, Verb, Noun Phrase, Prepositional Phrase, Adjective Phrase and Adverbial Phrase.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 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".