Short- and Long-Term Effects of Superhero Media on Young Children’s Risk-Taking Behaviors
Bibliographic record
Abstract
OBJECTIVE: Unintentional injuries, the leading cause of death for American children, are caused by a range of psychosocial factors, including risk behavior. One factor that may impact child risk-taking is modeling of superhuman risk-taking from superhero media, both immediately following superhero exposure and based on lifetime exposure and engagement. METHODS: Fifty-nine 5-year-olds were randomly assigned to view either a 13-min age-appropriate superhero television show or a comparable nonsuperhero show. After the viewing, children engaged in three risk-taking measures: (a) activity room, unsupervised play for 5 min with assortment of apparently dangerous items that might encourage child risk-taking; (b) picture sort, 10 illustrations of children in risk situations, with participant response concerning intended risk-taking in that situation; and (c) vignettes, 10 stories presenting situations with varying degrees of risk, with participant response on intended choice. Parents completed questionnaires concerning children's long-term superhero media exposure and individual superhero engagement (e.g., if child's most recent Halloween costume was of a superhero). Correlations and regressions evaluated effects of immediate superhero exposure, lifetime superhero exposure, and lifetime superhero engagement on children's risk-taking. RESULTS: Mixed results emerged. Lifetime superhero exposure was significantly related to children's risk-taking outcomes in two bivariate (vignettes and picture sort) and one multivariate (picture sort) model. Neither immediate superhero exposure nor lifetime superhero engagement was strongly related to risk-taking. CONCLUSIONS: Children's lifetime superhero exposure may influence children's risk-taking. Given American children's substantial media exposure, research should continue to unpack the role of superhero media on children's unintentional injury and other health risk behaviors.
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 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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".