Bibliographic record
Abstract
IntroductionThe abuse of the Internet by money launderers is potentially a significant threat (Solicitor General Canada, 1998; Veng Mei Leong, 2007). Why we cannot say is a significant threat? To date, there are only few criminal cases concerning so called cyberlaundering. But there are some symptoms observed by international organizations, law enforcement agencies, financial intelligence units and financial institutions. How we can explain that fact? Maybe it is not such a great tool after all as many people think or criminals does not trust new technologies or we are looking in the wrong direction? However criminals have been constantly seeking new ways to clean their illicit gains in order to stay ahead of law enforcement. Similar situation was in case of wire transfers in the 80's and 90's.Characteristics of Internet - What attracts launderers?There are few features of the Internet which attract criminals (including launderers) (Skalski, 2004; GIFI, 2008).AnonymityThe Internet seems to be a place where you can hide yourself among millions of other users; where you can pretend to be someone else since no one can truly identify you. But it seems that is no longer true, since there are some legal obligations put on Internet Service Providers to record and keep log files for a long period of time. They show which computer and when was connected to Internet. This measure is being used to fight computer crimes. It makes law enforcement's work to trace somebody's activity in cyberspace easier. Of course there are some means to circumvent them and to keep the anonymity. They include Internet Protocol (IP) spoofing, use of modem connections (every time user connects he gets different IP address), Wireless Fidelity technology which allows to abuse publicly open so called hot spots or unprotected routers to connect to the Internet (Ashwell, 2008), use of pre-paid phones as modem in order to connect to the Internet (it hides the identity of a user). Also the use of encryption technology (widely available on the Internet) and many proxy servers hinders the efforts of law enforcement to catch cybercriminals.No Face-To-Face contactsThis is called the depersonalization of financial operations. When we are using one of the financial services available on the Internet, we actually use our computer (and software) which connects to the bank's server. The whole process of placing orders (making requests) and executing them is fully (or partially) automatic without the presence of a human factor. So in fact we can very easily pretend to be some one else each time we visit bank in the cyberspace. The financial institution's server checks only two things the login (e.g. unique ID number) and the password - not the true identity of a customer. If the information is correct (meaning the same as the one stored in server's memory), the access is granted. As a result, it would be harder to detect and hold up transactions related to money laundering activities. It also cuts out another potential source of reporting suspicious transactions - financial institution employees (Cyber Laundering, 2002).Speed of the transactionsMoney laundering process would be less expensive and faster as the one using 'normal' or old-fashion transactions. New payment technologies permit to move funds more rapidly on long distances and make law enforcement work even more complicated (Williams, 1998). Some of them are instantaneous e.g. within one financial institution. It allows launderers to move funds very quickly within one country or even world wide. In essence it makes hiding the illicit source of money easier and difficult to trace. It makes also the whole procedure cheaper (Cyber Laundering, 2002).Globalization process: free movement of goods, services, people and new payment technologiesThe globalization of economy includes the necessity for people (entrepreneurs and customers) to move, invest and spend money wherever they want to. …
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".