Can Fantasizing While Listening to Music Play a Protective Role Against the Influences of Sensation Seeking and Peers on Adolescents’ Substance Use?
Bibliographic record
Abstract
"The combination of music and drugs proved to be potent, and scientific research has yet to explain it" (Levitin, 2008, p. 74; The World in Six Songs). This study examined if fantasizing while listening to music could represent a potential protective factor against adolescent substance use (cigarette, alcohol, and cannabis). The first hypothesis was that fantasizing while listening to music would moderate (buffer) the link between sensation-seeking and substance use. The second hypothesis was that fantasizing while listening to music would also moderate (buffer) the link between peer substance use and individual substance use. The sample comprised 429 adolescent boys and girls who answered a self-report questionnaire in 2003. They were regular students attending a public high school in Montreal, Canada. The results revealed that fantasizing while listening to music came short of buffering the link between sensation-seeking and substance use among highly musically involved adolescents. Still, fantasizing while listening to music significantly attenuated the relationship between peer substance use and individual substance use (thereby, showing a protective effect) among highly musically involved adolescents. Fantasizing while listening to music did not buffer the relation between either risk factor (sensation-seeking or peer substance use) and substance use among moderately musically involved adolescents.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".