Bibliographic record
Abstract
On November 15, 2018 the Canadian Association for Security and Intelligence Studies (CASIS) Vancouver hosted its tenth roundtable meeting which covered “The Role of the dark web in the Crime and Terrorism Nexus.” The presentation was hosted by Dr. Richard Frank, an assistant professor in the School of Criminology at Simon Fraser University, as well as the Director of the International CyberCrime Research Centre (ICCRC). In the presentation, Dr. Frank began by explaining the operations of the dark web, and then moved on to discuss why the dark web cannot just be shut down, as well as actions law enforcement (policing) could take in order to counter the activities on the dark web. The subsequent roundtable discussion opened with an analysis of the operations of Silk Road, an online marketplace on the dark web that specializes in the sale of illegal drugs, weapons, and stolen identities. The topics of interest in the discussion were the effects of internet-based trade of illicit goods on organized crime and local drug markets, in addition to whether the dark web can be used constructively.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.007 | 0.019 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".