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Record W6924723846 · doi:10.15496/publikation-28956

Mass-Marketing Fraud: A Threat Assessment

2010· other· en· W6924723846 on OpenAlexaboutno aff

Bibliographic record

VenueUniversitätsbibliothek Tübingen · 2010
Typeother
Languageen
FieldAgricultural and Biological Sciences
TopicAquaculture Nutrition and Growth
Canadian institutionsnot available
Fundersnot available
KeywordsLaw enforcementScope (computer science)LegislatureVariety (cybernetics)EnforcementLegislationValue (mathematics)Phenomenon

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.049
Threshold uncertainty score0.951

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0500.000

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.

Opus teacher head0.010
GPT teacher head0.220
Teacher spread0.210 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2010
Admission routes1
Has abstractyes

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