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Record W7115906269 · doi:10.30872/jbssb.v7i3.8800

Character Development of Peter in J.M Barrie "Peter and Wendy" Novel

2023· article· W7115906269 on OpenAlexaboutno aff

Bibliographic record

VenueIlmu Budaya Jurnal Bahasa Sastra Seni dan Budaya · 2023
Typearticle
Language
FieldArts and Humanities
TopicLiterary Theory and Cultural Hermeneutics
Canadian institutionsnot available
Fundersnot available
KeywordsCharacter (mathematics)Character developmentStructuralism (philosophy of science)Plot (graphics)Interpretation (philosophy)

Abstract

fetched live from OpenAlex

Character development is the transformation process of the character from bad character to good character or the opposite that influenced by intrinsic elements. That is why the researchersused the guide from intrinsic elements and used the structuralism theory from Seymor Chatman. In this research, the researchers focused on the development of Peter, the main character. The researchers also used characterization theory from Rimmon Kenan to characterized Peter. This research used qualitative content analysis as the research design. The result of the research showed that even though many says that Peter is a flat character that impossible to change, the researcher found that the influence of Wendy towards Peter, she changed him into a better version of himself, appreciated the lost boys, and even though his thought about parents was not good, he still let the lost boys to leave and have their own parents. The researchersalso found influence of intrinsic elements on Peter’s character development. First, plot showed his condition from the beginning until the ending events. Second, setting showed his background in Neverland, the place where he lived.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.857
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.031
GPT teacher head0.240
Teacher spread0.209 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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