Are tattooing and body piercing indicators of risk‐taking behaviours among high school students?
Bibliographic record
Abstract
PURPOSE: To date, studies pertaining to possible links between body modification and risk-taking behaviours have been conducted mainly among targeted groups. The objective of this study is to examine the influence of a number of risk-taking behaviours on the probability of being pierced or tattooed among a general adolescent population. METHODS: Data come from a cross-sectional study conducted among a sample of 2180 students aged 12-18. Data were collected directly from students through a self-report survey. RESULTS: Findings confirm the "risky" nature of these practices even though the tattooed and pierced subjects of this study were from a general adolescent population. Factors that contribute significantly to the likelihood of teenagers being tattooed or pierced, for both genders, are associated with "externalized risk behaviours" such as multiple drug use, illegal activities, gang affiliation, problem gambling, school truancy and rave attendance. CONCLUSION: Nowadays, tattooing and body piercing are perceived by many as body decoration, increasingly belonging to the realm of generational conformity. Contrary to this view, our results suggest that these practices among adolescents are mostly adopted by those who are involved in various deviant or illegal activities, which are often interrelated.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".