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.
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 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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.001 |
| 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".