Bibliographic record
Abstract
Animation is a powerful tool for engaging audiences in a fun and innovative way, especially in the context of animated children’s films. These films use vibrant visuals, imaginative storytelling, and relatable characters to connect with young viewers, making complex themes more accessible and engaging. This is particularly evident in Domee Shi’s Turning Red (2022), where animation is a medium for entertainment and a way to explore complex psychological and cultural themes. In the film, 13-year-old Meilin Lee, a Chinese-Canadian girl, transforms into a red panda whenever her emotions become overwhelming. The plot centers around an ancient curse passed down to Mei, causing her to change into a large, hairy, sweaty, and stinky red panda—utterly contrary to the traditional expectations of a 13-year-old girl. Using animation to explore these profoundly emotional themes, Turning Red presents a powerful metaphor for the psychological consequences of suppressing emotions, particularly anger, to conform to cultural and familial expectations. The film explores the impact of generational trauma on identity and emotional development, showing how these unresolved issues are passed down and affect one’s sense of self. Through its creative animation and vivid storytelling, Turning Red offers a meaningful exploration of self-acceptance and emotional expression. This paper will analyze the film’s depiction of generational trauma, examining how emotional suppression and the pressure to conform impact Mei’s identity and development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".