Peter Pan Goes to War: the Reimaging and Exploration of J.M. Barrie’s Story as a Historically Realistic Graphic Novel
Bibliographic record
Abstract
The adaptability of stories helps ensure their survival in the popular consciousness and fairy tales in particular exhibit this characteristic. The numerous retellings, reinterpretations and recreations of these beloved tales ensure that they endure to enchant a new generation. One such fairy tale which continues to lend itself to adaptation is J.M. Barrie’s elusive creation, Peter Pan. Kurtis J. Wiebe and Tyler Jenkins’s graphic novel, Peter Panzerfaust (2012–2017), is one of the most recent retellings of this beloved tale, and forms the focus of this chapter. In this story, Peter is removed from the fantasy of make-believe and transposed onto the realistic historical setting of wwii. I consider various instances throughout the graphic novel that have appropriated Barrie’s elements from the Peter Pan story and reinterpreted and recreated these elements to suit not only the historical wartime milieu, but also the graphic novel as new medium. Hutcheon’s adaptation theory and how adaptations are a form of palimpsest will form the basis for interpreting the interplay between the Peter Pan story and the graphic novel through the analysis of features such as dialogue and imagery (particularly that of Peter Pan) thereby showing that through this process of recreation and reinterpretation of the original text, this graphic novel can be seen as a successful adaptation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.026 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".