Exploring Children's Preferences for Media Characters in Relation to Gender Roles and Stereotypes
Bibliographic record
Abstract
Children learn about gender and gender roles from various sources, including screen media. They also display gender-based or gender-stereotypical preferences in social contexts. Recent work shows that children's media is largely made up of gender-stereotypical characters. An intriguing question arises as to whether children themselves hold gender-stereotypical beliefs about media characters. The present research aimed to address this question in two studies (four experiments). In each experiment, the researcher explained that they were creating a brand-new television show and needed help choosing characters for the show. Then, children aged 4-5 and 7-8 years were presented with a pair of characters and asked who they thought would be best at teaching a stereotypically feminine domain or a stereotypically masculine domain in the show (Study 1), or who would be best at solving a problem in an episode vignette using one of four strategies: magic, talking, STEM, and physical power (Study 2). In Study 1, both age groups were more likely to choose female characters and female characters with a stereotypically feminine appearance (i.e., wearing a dress) to teach stereotypically feminine domains, but only 7- to 8-year-olds chose male characters and female characters with a counter-stereotypical appearance (i.e., wearing pants) to teach stereotypically masculine domains. In Study 2, children thought female characters would be best at solving problems using magic and talking, whereas male characters would be best at solving problems using physical power. In addition, 7- to 8 year-olds thought that female characters with a counter-stereotypical appearance would be best at solving problems using STEM or physical power. Given that children frequently engage with and learn from media characters, these results highlight the importance of creating multi-dimensional and complex characters that are not only defined by their gender or appearance.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".