Analysis of the Bases of Power of Key Players in the Industry of Novel Psychoactive Substances
Bibliographic record
Abstract
BACKGROUND: The study of novel psychoactive substances (NPS) should be at the intersect of neuroscience, psychology, social science, data science, information and communication technology, policy-making, and legislation. The amalgamation of social science should be widely implemented; the concept of the individual basis of power (authority) has been applied in this study; the aim was to quantify the magnitude of authority for the major players in connection with the NPS industry.MATERIALS & METHODS: Data were collected for NPS researchers and e-vendors. Concerning the NPS scientists, fifty researchers were randomly picked using a random number generator. For each researcher; a power score was calculated; the power scoring is representative for the individual basis of power. There will be a kindred analysis for e-vendors on the darknet; the power scoring will rely on; e-vendor level, trust level, duration of membership in the e-market (vendor's antiquity), number of positive and negative feedbacks from e-customers, number of sold substances, number of subscribers, and e-vendor’s scoring on Grams search engine.RESULTS: Unfortunately, the summative power scores of NPS protagonists were higher than those who oppose and regulate the NPS phenomenon. Terrorist organisations were found to possess the highest power scores due to the additional use of illegal tactics. Power scoring for NPS researchers was highest in Europe, particularly the in the UK and Italy. On the other hand, e-vendors’ power scoring was highest for the AlphaBay e-market of the darknet.CONCLUSION: Principles of social science and psychology should be integrated into the collaborative efforts of NPS researchers. This study proposes a novel method to assess the authority of NPS-related personnel existing within the virtual space of the web; its applications are not limited to NPS researchers and e-vendors but can also be applicable for; e-markets, e-customers and (ab)users, and policy makers.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.000 |
| 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".