GENDERED LEISURE ACTIVITY BEHAVIOR AMONG NORWEGIAN ADOLESCENTS ACROSS DIFFERENT SOCIO-ECONOMIC STATUS GROUPS
Bibliographic record
Abstract
The present paper explores gendered behavior based on participation patterns of leisure activities among adolescents across socio-economic status (SES) groups, aiming to increase knowledge and understanding of how gender socialization processes are expressed through boys’ and girls’ participation in leisure activities. Furthermore, the aim is to investigate whether such gendered behavior is associated with general levels of participation, and if there may be differences between low, middle, and high SES groups. The study used Norwegian nationally representative data of 15- and 16-year-olds from the World Health Organization's cross-sectional survey, <em>Health Behaviour in School-aged Children 2005/06</em> (<em>n</em> = 3,273). Logistic regression analysis and propensity scores showed that adolescent boys and girls had clear gendered behavior patterns based on leisure activity participation in 27 different activities, and that boys and girls had about equal distributions on what was classified as typical (“boyish” or “girlish”), atypical, and neutral gender behavior. Furthermore, gendered behavior was correlated with higher levels of participation for both sexes, and it was more or less similar for boys and girls and for different socio-economic status groups. However, when investigating differences in distributions between SES groups, high SES girls were found to have a significantly higher prevalence of both “boyish” and “girlish” activity behavior, but less “gender-neutral” compared with girls in medium and low SES groups. There were no such differences for boys.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".