Gender Representation in Classic Fairy Tales: A Comparative Study of Snow White and the Seven Dwarfs, Cinderella and Beauty and the Beast
Bibliographic record
Abstract
Grimm’s Snow White and the Seven Dwarfs, Cinderella, and De Beaumont’s Beauty and the Beast are three examples of classic fairy tales that have been commonly told to children. The writers focused the study on the portrayal of gender representation reflected in these fairy tales. The writers used the descriptive qualitative method and feminist theory to analyze how these fairy tales portray gender representation. This study was expected that it could contribute to gender role discussion in children's literature and introduce children to equal gender roles to make them able to treat different gender equally. Unlike previous studies, this research focuses on traditional fairy tales and employs a qualitative methodology that involves close reading and content analysis. The writers found out that Grimms’ Snow White and the Seven Dwarfs and Cinderella portray traditional gender stereotypes. Snow White and Cinderella support the domination of masculinity and submissive femininity, while Beauty and the Beast does not portray the traditional gender roles because the tale makes its female protagonist free to determine her life. The writers used a feminist point of view to analyze gender representation in the selected tales. It is expected that this study highlights the importance of critically analyzing gender roles in children's literature and the need for more diverse and complex representations of gender in fairy tales and other literary works.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.018 | 0.014 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".