Muscle-Building Exercise and Weapon Carrying and Physical Fighting Among U.S. Adolescent Boys
Bibliographic record
Abstract
= 4120). Muscle-building exercise was assessed based on the number of days reported in the past 7 days, recategorized into four levels of engagement (no engagement [0 days], low engagement [1-2 days], moderate engagement [3-5 days], and high engagement [6-7 days]). Three forms of weapon carrying (general, on school grounds, gun carrying) and two forms of physical fighting (general, on school grounds) were assessed. Five logistic regression analyses with adjusted odds ratios (AOR) and 95% confidence intervals (CI) were used to determine the association between engagement in muscle-building exercise and weapon carrying and physical fighting, while adjusting for relevant demographic and control variables. Over 75% of participants reported engaging in muscle-building exercise. One in five (19.8%) participants reported any general weapon carrying in the past 30 days, 3.3% reported any weapon carrying at school in the past 30 days, 6.5% reported any gun carrying in the past 12 months, 28.0% reported any general physical fighting in the past 12 months, and 10.7% reported any physical fighting at school in the past 12 months. Logistic regressions showed that, compared to no engagement, participants who reported high engagement of muscle-building exercise had higher odds of general weapon carrying (AOR 2.18, 95% CI 1.54-3.07), gun carrying (AOR 2.12, 95% CI 1.23-3.64), and general physical fighting (AOR 2.07, 95% CI 1.53-2.79). These are novel findings that add to a growing literature related to engagement in muscularity-oriented behaviors among males. Prevention and intervention efforts are needed to ensure that adolescent boys engage in muscle-building exercise in ways that are not harmful and to reduce weapon carrying and physical fighting.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".