Trends in substance references in Australian top 20 songs between 1990 and 2015
Bibliographic record
Abstract
INTRODUCTION AND AIMS: This study examined references to alcohol and other drugs in top 20 songs over the last quarter of a century to explore the potential for popular music to constitute a barometer for changes occurring in youth consumption of alcohol and other substances. DESIGN AND METHODS: The online Australian Recording Industry Association charts resource was accessed to identify the top 20 songs for the period 1990 to 2015 inclusive. The lyrics of the identified songs were imported into NVivo11 for coding and analysis. Two coders analysed each song by line unit and a third coder assisted in resolving any coding discrepancies. RESULTS: Of the 508 discrete songs, 74 (15%) featured references to alcohol, tobacco and/or illicit drugs. Substance mentions increased over time such that the second half of the study period accounted for three-quarters of all references. The peak period for mentions was 2008-2012, with 2010 exhibiting an especially high prevalence rate for alcohol references. There was a marked decline in alcohol mentions between 2010 and 2013. The rate at which female artists referred to alcohol increased sharply until 2010 and then decreased. DISCUSSION AND CONCLUSIONS: Patterns in substance mentions in top 20 songs in more recent years may reflect broader social trends that influence youth substance use. As such, monitoring music lyrics may assist researchers to better understand forces underlying patterns of youth substance use.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".