Mass-Marketing Fraud: A Threat Assessment
Bibliographic record
Abstract
Mass-marketing fraud is a term increasingly used around the world to refer to fraud schemes that use mass-communications media – including telephones, the Internet, mass mailings, television, radio, and even personal contact – to contact, solicit, and obtain money, funds, or other items of value from multiple victims in one or more jurisdictions. Although law enforcement and regulatory authorities often use a variety of names to refer to the phenomenon – including “advance-fee fraud,” “419 fraud,” “Internet fraud,” and “telemarketing fraud” – the growing profusion of labels for these fraud schemes tends to obscure the fact that such schemes often are conducted using multiple communications channels to identify and contact victims, as well as identical or highly similar methods of operation that are not dependent on a single communications medium. Today, mass-marketing fraud schemes operate from, and increasingly seek to target victims in, numerous countries on multiple continents. Moreover, such schemes are aware and take advantage of differences between countries in legislative authorities prohibiting such schemes. As a consequence, mass-marketing fraud has become a substantial concern for law enforcement in several regions of the world. The International Mass-Marketing Fraud Working Group (IMMFWG) prepared this threat assessment to provide governments and the public with a current assessment of the nature and scope of the threat that mass-marketing fraud poses around the world. The IMMFWG, which was established in September 2007, consists of law enforcement, regulatory, and consumer protection agencies from seven countries, including Australia, Belgium, Canada, the Netherlands, Nigeria, the United Kingdom, and the United States, as well as Europol. The IMMFWG seeks to facilitate the multinational exchange of information and intelligence, the coordination of cross-border operations to detect, disrupt, and apprehend mass-marketing fraud, and the enhancement of public-awareness and public-education measures concerning international mass-marketing fraud schemes. The information and analysis in this assessment is current through May 2010, and are derived principally from public and non-public law enforcement and non-law enforcement sources in Australia, Belgium, Canada, the Netherlands, Nigeria, the United Kingdom, and the United States.
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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 | 0.013 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.019 | 0.006 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".