Suffixes Forming Adjective Found in the Novel Peter Pan
Bibliographic record
Abstract
This study aims to describe the suffixes forming adjective found in the Novel Peter Pan and the bases that are possibly changed into adjective. The data of this research were taken from the novel entitled Peter Pan by J.M Barrie. The main theory which is used in analyzing the data is theory of suffixes by Bauer (1983). The data were collected by reading. After collecting, the data were classified based on scope discussions. The result showed that there are six kinds of part of speech that were found, namely; noun, verb, adverb, adjective, conjunctions, pronoun. Suffix forming adjective is the process of forming adjective by adding suffixes to another word class. In the data, it is found that there are three kinds of word class bases that can be attached with suffixes to form adjective, they are noun base, verb base, and adjective base. Furthermore, based on the result of the research which was described descriptively, it can be concluded that suffixes forming adjective there are some points that can be taken as the conclusion. There are six kinds of part of speech that were found, namely; noun, verb, adverb, adjective, conjunctions, pronoun. Suffix forming adjective is the process of forming adjective by adding suffixes to another world class. In the data, it is found that there are three kinds of word class bases that can be attached with suffixes to form adjective, they are noun base, verb base, and adjective base.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| 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".