Why Do Girls Play? Strength And Competitiveness But Not 2d
Bibliographic record
Abstract
Women continue to participate in sport at a lower rate than males at all ages. Girls who participate in sport gain many advantages (e.g. better bone health, greater cardio- respiratory fitness, better mental health). However, even with programs designed to emphasize participation, some women choose to continue sport participation, while others do not. Given the potential advantages and controversies currently surrounding testosterone and female sport participation, it is possible that this hormone may predispose women toward sport participation. PURPOSE: To determine if the 2nd to 4th digit ratio (2DR) correlates with sport participation throughout adolescence and young adulthood in women. METHODS: A cross- sectional analysis of indirect prenatal androgen concentrations (i.e. 2DR) was obtained from 92 females (aged 18-30y). Participant demographic, anthropometric, behavioural, and retrospective sport participation information were collected on one occasion. RESULTS: 2DR was not significantly correlated with total sport participation (r = -0.650, p = 0.538). Secondary analysis revealed significant correlations between sport participation and max hand grip (r=.406, p = 0.000), sport competitiveness (Sport Orientation Questionnaire) (r = 0.475, p = 0.000) and Sport Aggression (Scale of Children’s Action Tendencies in Sport) (r = 0.240, p = 0.021). CONCLUSION: While 2DR does not, strength and the sport specific behavioural traits of competitiveness and aggression are able to predict retrospective sport participation. However, causality of these relationships could not be determined because some traits are likely strengthened through sport participation and androgens have been linked to strength, competitiveness, and aggression. Given that females participate in sport at lower rates than males, and that sport provides multiple social and health advantages, continuing to determine what factors influence female sport participation is necessary.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".