EXPLORING THE NARRATIVE STRUCTURE TRANSFORMATION OF “PETER PAN AND WENDY” NOVEL (1911) AND MOVIE ADAPTATION (2023)
Bibliographic record
Abstract
Narrative structure refers to the foundational framework that dictates how a story is conveyed to its audience, whether through reading, listening, or viewing. This research explores the transformation of the narrative structure in the adaptation of J.M. Barrie's novel "Peter Pan and Wendy" (1911) into the 2023 movie produced by Walt Disney Pictures. Utilizing ecranisation theory and Caroline Denton's (2007) narrative structure framework, the study examines the transformation on five key narrative elements such as setting, characters, conflict, climax, and resolution. This study utilizes a qualitative content analysis method to identify the transformation that occurred. The research reveals significant changes, including the reduction of characters from 27 to 23 and adding 2 new characters, 9 setting of place in the novel are reduced to only 6 in the movie, the setting of time in the movie is shorter compared to the novel, and modify the plot development. The findings show that while the novel and movie share a common storyline, their narrative structures diverge considerably, reflecting distinct storytelling techniques and creative choices. This study provides insights into the adaptation process, highlighting how filmmakers balance fidelity to the source material with the need for innovation to suit the cinematic medium. By analyzing these transformations, the research contributes to a deeper understanding of how classic literary works are reimagined for contemporary audiences through movie.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".