Rebelliousness, Effortful Control, and Risky Behavior: Metamotivational and Temperamental Predictors of Risk-Taking in Older Adolescents
Bibliographic record
Abstract
Adolescence is frequently regarded as a time of increased vulnerability to engaging in risky behaviors such as binge drinking, unsafe sexual activities, and illicit drug use.The present study examined risk perception and risk-taking behavior in older adolescents from two different perspectives, by examining temperamental and metamotivational predictors of likelihood of engaging in risky activities.A sample of 76 undergraduate students aged 17 to 19 years completed a questionnaire package that included the Motivational Style Profile, Rebelliousness Questionnaire, the short form of the Adult Temperament Questionnaire, and the expected risk and expected involvement subscales of the Cognitive Appraisal of Risky Events.Findings indicated that rebelliousness and effortful control (i.e., ability to appropriately regulate attention and behavior) were strong predictors of expected involvement in risky behaviors, and that proactive rebelliousness was a particularly influential predictor of illicit drug use, risky sexual activities, aggressive and illegal behaviors, and risky academic and work behaviors.In addition, a number of significant correlations between temperamental variables and metamotivational dominance were observed, lending empirical support to reversal theory's metamotivational constructs and their measurement.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".