It's a Bird! It's a Plane! It's...a Girl?!: Analyzing Representations of Femininity on The CW's Supergirl
Bibliographic record
Abstract
Although it takes more than diverse media representation to create positive social change, representation is still an important part of normalizing identities and pushing discussions of social issues.However, representations of certain minority groups, whether on the basis of racial, ethnic, sexual, or gender identities, are often largely absent in media, and flawed when they do exist.This is true of comic books and the various forms of media that have adapted superhero stories.For instance, superhero tales have long been dominated by depictions of superheroes who are heterosexual cisgender men.Because of this trend spanning from the 1930s into the 21 st century, superhero stories have developed a reputation of being a boy's club rife with sexism.Certainly, this issue persists to this day, but this thesis provides a snapshot of how women are fairing in superhero media in the mid-to-late 2010s.By focusing specifically on the case study of the CW's Supergirl, I discuss how representations of superwomen have improved greatly compared to popular expectations and no longer entirely reflect a singular and problematic ideal of what being a woman means.In particular, my examination is centered around three of Supergirl's main women: protagonist Kara Danvers/Supergirl, Kara's sister and super spy Alex Danvers, and the heroic Nia Nal/Dreamer.By doing an in-depth analysis of how femininity is represented through these three women, I argue that the show presents complex and nuanced depictions of femininity that are a strong step forward for the genre.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".