Bibliographic record
Abstract
The paper discusses how Turning Red (2022) works out various crises that center on the identity crisis of the female protagonist, Meilin Lee (Mei). At the core of conflicts lies the question of which identity and which way of life to choose: the “ancestral” Eastern, Chinese one or the modern Western, North-American one. This analysis presents how this Disney/Pixar animated film addresses the questions of multicultural, dual, hyphenated, diasporic identities as well as cross-generational conflicts through displacement. Mei Mei has to decide if she keeps her red panda (her Chinese part) or cuts herself off of it enclosing it into a talisman while leading an entirely American/Canadian way of life. Her choice is both, a decision that none of the women in her family made before her. Turning into a red panda can both be a curse and a blessing for the female family members and it seems that all of these women viewed it as a curse and a burden before Mei Mei reinterpreted it. While fighting red panda, all female family members have to revisit their own ‘red pandas’ thus solving not only Mei Mei’s identity problems but also questions of agency, including those of her mother, which affects all female family members leading finally to reconciliations. The solution to this identity struggle and to the cross- and transgenerational/cross- and transcultural fights over meanings and identities is resolved with the help of humor and peer support.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".