“Spice,” “Kryptonite,” “Black Mamba”: An Overview of Brand Names and Marketing Strategies of Novel Psychoactive Substances on the Web
Bibliographic record
Abstract
UNLABELLED: Abstract Introduction: Novel Psychoactive Substances (NPSs) are often sold online as "legal" and "safer" alternatives to International Controlled Drugs (ICDs) with captivating marketing strategies. Our aim was to review and summarize such strategies in terms of the appearance of the products, the brand names, and the latest trends in the illicit online marketplaces. METHODS: Scientific data were searched in PsychInfo and Pubmed databases; results were integrated with an extensive monitoring of Internet (websites, online shops, chat rooms, fora, social networks) and media sources in nine languages (English, French, Farsi, Portuguese, Arabic, Russian, Spanish, and Chinese simplified/traditional) available from secure databases of the Global Public Health Intelligence Network. RESULTS: Evolving strategies for the online diffusion and the retail of NPSs have been identified, including discounts and periodic offers on chosen products. Advertisements and new brand names have been designed to attract customers, especially young people. An increased number of retailers have been recorded as well as new Web platforms and privacy systems. DISCUSSION: NPSs represent an unprecedented challenge in the field of public health with social, cultural, legal, and political implications. Web monitoring activities are essential for mapping the diffusion of NPSs and for supporting innovative Web-based prevention programmes.
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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".