The NPS Phenomenon and the Deep Web: Internet Snapshots of the Darknet and Potentials of Data Mining
Bibliographic record
Abstract
BACKGROUND: The illegal electronic trade of NPS substances on the deep web and the darknet have never been thoroughly mapped. This study will propose and illustrate a blueprint for mapping of the darknet e-marketplace, including activities originating from the Middle East.MATERIALS & METHODS: Multiple Internet snapshots were taken for the darkest e-marketplace, e-markets, Grams search engine, and e-vendors. In relation to the most popular and high-risk NPS substances, the most dominant e-market will be identified. Special correlation will be carried out with the; population count of shipping countries of NPS, the incidence of rape and sexual assaults, and religious affiliation.RESULTS: The most popular high-risk NPS were identified; cannabis and cannabimimetic, MDMA, crack, Meth, and LSD. These were geo-mapped primarily into; Netherlands, US, UK, Germany, Australia, Canada, France, and Spain. AlphaBay e-market was found to be a proper representative for the darknet e-marketplace; the main advertised NPS were categorised into cannabis and cannabinoids (1), stimulants (2), empathogens (3), psychedelics (4), benzodiazepines (5), opioids (6), and prescription-related substances (7). The contributing Middle Eastern and Arabic countries included; UAE, Oman, Morocco, Egypt, and Cyprus.CONCLUSION: The e-commerce activities on the darknet have been ever evolving. Future attempts to study this e-marketplace should be innovative and rely on statistical inference. A blueprint is required for geo-mapping of the shipping countries, including those from the region of the Middle East. Principles of social sciences, including the analysis of the individual basis of power, should be considered.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| 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".