“Don´t grow up, it´s a trap” : Den androgyne protagonisten Peter Pan
Bibliographic record
Abstract
The aim of this study was to examine how the protagonist Peter Pan was portrayed based on gender stereotypes in the adaptation from 2003. Furthermore, the purpose was to study if children may be affected by these stereotypes. The research questions are: How is the protagonist Peter Pan portrayal in a stereotypical way? How can children be affected by the eventual stereotypes? Peter Pan is a fictive character created by author J.M Barrie. He was first created for the book “The Little White Bird” released in 1902. The story contains several characters, for instance Wendy, John and Michael Darling, Tinkerbell, and the antagonist of the story: Captain Hook. The method that was used was a qualitative content analysis of the movie Peter Pan via the streaming site Viaplay. Seven key scenes and six scenes have been chosen to discuss whether Peter is portrayed in a stereotypical way or not. The approach to complete this study was through semiotics and five different units to analyze the scenes. These were body language, dialog, choice of words, costume, and environment. Information was gathered from reliable sources of articles, websites, and literature. There were two question formulations that were answered in this study. Finally, the analysis was discussed to determine whether Peter is a masculine, feminine or androgynous character. To conclude, Peter mostly is a masculine character with feminine features, therefore an androgynous character. Children may indeed be affected by the stereotypes that is applied on Peter in the adaptation, due to television´s big impact on its spectators.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".