Double Trouble: Gender Fluid Heroism in American Children’s Television
Bibliographic record
Abstract
Abstract Gender fluidity makes only rare appearances on North American television, and remains almost completely absent from programming for children. In contrast, transgender characters are making inroads into mainstream North American TV for adults. Still, media depictions of transgender people in the late 1990s and early 2000s have largely shown them as aberrations, having illegible and/or unstable identities, joining mainstream Euro North American society which tends to medicalize and pathologize transgender identities. Thus, too often the representation provided serves only to reinforce binaries by making the character exceptional and noting their unconventionality, or to highlight gender fluidity as a problem. Examining the animated streaming TV series She-Ra and the Princesses of Power (2018–2020), we use scholarship on gender fluidity to critique the show’s representations of genders in addition to and beyond male and female. Looking at She-Ra through this lens, the show challenges assumptions about princesses, villains, helpers, and heroes. Ultimately transgressing traditional categories, the princesses and their allies, in their own distinct embodiments and self-presentations, use their differing magical and other skills to fight enemies in the Evil Horde to protect their planet, Etheria.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".