MétaCan
Menu
Back to cohort
Record W4414788539 · doi:10.1080/15332640.2025.2560947

Dark times & starry eyes: Substance use themes in the Weeknd’s discography

2025· article· en· W4414788539 on OpenAlexaffabout
Ayomide Fakuade, Karim Mukhida

Bibliographic record

VenueJournal of Ethnicity in Substance Abuse · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicMusic History and Culture
Canadian institutionsDalhousie UniversityUniversity of Calgary
Fundersnot available
KeywordsNarrativeSubstance useThematic analysisLyricsRomanceContent analysisCoping (psychology)Discography

Abstract

fetched live from OpenAlex

Societal preoccupations are manifest in popular culture, including music. Canadian award-winning musician, The Weeknd, explores substance use and pain in his discography. This study employs inductive thematic analysis to explore how substance use and pain are referenced in his songs. Lyrics from his five studio albums and three mixtapes were reviewed, revealing 399 substance use references. The analysis revealed seven overarching themes, including substance use as a symbol of celebration and luxury, its role in coping with hardship but also potentially leading to problematic use and romantic turmoil. Uncovering these recurrent themes highlights how the narratives that The Weeknd's music constructs around substance use. Knowledge of these themes and narratives could be leveraged in educational initiatives to engage a wide range of trainees on substance misuse, addiction, and its societal impact.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0110.010
Scholarly communication0.0050.002
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.049
GPT teacher head0.270
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Ethnicity in Substance AbuseSame topicMusic History and CultureFrench-language works237,207