Between Pleasure and Punishment: The Moral Vocabulary of Drug Use in Nigerian Digital Spaces
Bibliographic record
Abstract
Drug use among Nigerian youth is increasingly treated as both a public health crisis and a moral failing. This study examines how the crisis is narrated in digital spaces through slang, sentiment, and moral language that not only reflect consumption patterns but shape them. We analyzed 5,240 text samples: Twitter posts (n = 3,100), Nairaland threads (n = 920), and lyrics from 45 hip-hop and street-pop tracks (n = 1,220 lines), collected between January 2021 and March 2024. Using targeted keywords including “high”, “loud,” “colorado,” “skushi,” “roachies,” “tramadol,” and “codeine.” Using sentiment analysis (VADER), an adapted moral foundations tagging approach, and close qualitative coding, we traced how different drugs are framed across class, gender, and emotional registers. Stimulants such as “loud” and “tramadol” are often aligned with masculinity, performance, and aspiration, while sedatives like “codeine” and “rohypnol” appear more often in narratives of feminine withdrawal, vulnerability, or social regulation. Imported substances (e.g., “Canadian Loud,” “SK”) evoke status and trend-savviness; locally mixed drugs (e.g., “monkey tail,” “gutter water”) mark degradation, addiction, or spiritual decline. First-person disclosures carry tones of defiance or fatalism, while third-party accounts invoke disgust, fear, and betrayal of purity or religious authority. These moral framings do not merely describe behavior; rather, they regulate it. If public health is to be effective, it must move beyond condemnation and engage with the lived vocabularies of those it seeks to reach.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".