Native American Stereotyping in Peter Pan: A Modern Solution?
Bibliographic record
Abstract
Over the past few decades, readers and viewers have become more critical about representations of minority groups, including representations of gender, race, and ethnicity. This has forced makers of new adaptations to critically think about how to adapt certain aspects of their older source text. This thesis investigates such an adaptation, namely the 2015 film Pan (directed by Joe Wright, based on J.M. Barrie’s Peter Pan (1911)), which took a transcultural approach and transformed the controversial Native American tribe into an abstract, imagined, and multicultural community. Through close reading and close viewing, this thesis provides an analysis of the representation of the tribe in both Peter Pan and Pan and shows that despite Wright’s efforts to remove the Native American aspects of the story, the film still includes various Native American stereotypes and a white supremacist discourse, similar to those present in the novel. While this particular version does thus not succeed at removing these contested aspects, this thesis does argue that the approach could still be favourable but suggests further research is needed.
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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 | 0.004 |
| 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 | 0.013 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".