Deconstruction of Peter Pan’s Character in Edward Kitsis’ and Adam Horowitz’s Once Upon a Time, Season Three (2013)
Bibliographic record
Abstract
The purpose of this research is to find out the deconstruction of Peter Pan’s character in Kitsis’ and Horowitz’s work as described in Barrie’s Peter Pan. Kitsis’ and Horowitz’s Once Upon a Time, Season Three is the first film that deconstructs the character of Peter Pan from Barrie’s Peter Pan. The qualitative method is used in analyzing the main data that are taken from both works, Barrie’s Peter Pan and Kitsis’ and Horowitz’s Once Upon a Time, Season Three. The data analysis starts by selecting the data from re-watching and re-reading the works. Then analyzing them using the theory of deconstruction, character and characterization and cinematography. The theory is used to find the binary opposition and analyzing the characteristics of Peter Pan in both works. The cinematography is also needed to support the analysis and strengthens the argument of the analysis from the character’s deconstruction. The result of the analysis shows that the characteristic of Peter Pan in Barrie’s Peter Pan is deconstructed from hero into villain. It shows that there are four characteristics of Peter Pan as a hero that are deconstructed, namely, honest, fearless, polite and caring. Those characteristics are deconstructed into the character of Peter Pan as a villain who is manipulative, fearful, impolite and selfish. The four characteristics that are deconstructed can be seen from Peter Pan’s action towards other characters, from other characters’ explanation or the character’s emotions through every relevant scene in the film.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".