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Record W247761429

CONSIDERATIONS ON THE ECONOMIC AND FINANCIAL CRIME INVESTIGATION PHENOMENON PECULIAR TO EUROPEAN JUDICIAL SCOPE

2012· article· en· W247761429 on OpenAlexaboutno aff
Ion Rusu, Dorin Matei, Varvara Licuţa Coman, Tache Bocănială, Mirela Paula Costache

Bibliographic record

VenueContemporary Readings in Law and Social Justice · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicRussia and Soviet political economy
Canadian institutionsnot available
Fundersnot available
KeywordsMoney launderingPhenomenonBusinessFinancial crisisLanguage changeEconomicsFinanceEconomic policy
DOInot available

Abstract

fetched live from OpenAlex

ABSTRACT.The recent economic and financial crisis, with little chance of being exceeded in short term, has grown the economic and financial phenomenon, exceeding the national authorities due to super state institutions, structures and computerization on a large scale; the wide range of offenses, such as EU subsidies fraud, cross-border smuggling, laundering black money, computer crimes, etc. impose countermeasures going from prevention to their coercion in ways to reduce or even stop the phenomenon, which seems to be always one step ahead of the law. Accomplishing preliminary acts of criminal prosecution, investigation methods, evidence, preventive and protective measures must take the economic and financial phenomenon of at higher levels.Keywords: coercion, investigation, methodology, prevention, scope1. IntroductionThe economic and financial specific for the beginning of the third millennium is characterized by accelerated globalization and unprecedented dynamism, especially of large-scale fraud performed by specialists. Amid an evolving economic and financial crisis, which started in 2008 in the U.S., has not spared Europe, mainly affecting a series of banks and the economies of most countries, the modern organized tends to grow continuously, as they engage in all forms of economic-financial, legal or illegal activities, the essential condition being that they have to be profitable, providing the possibility of bringing into the legal system channel the obtained funds and getting the grouping influence and power to a higher level, using any means, especially corruption.According to the studies conducted in the United States of America, it was listed the organized in matters among the threats to national security. The report presented by Louis Freeh, director of the FBI in October 1997, at the U.S. Congress, highlighted the fact that criminal organizations are particularly dangerous because it involves computer science, encryption technique and the use of money laundering structure for recycling hundreds of million dollars. In his opinion, the organized groups operating in the U.S. come from Russia, Central and Eastern Europe, Asia, Africa and other parts of the world (Conseil de G Europe, Doc. 7971/ 22.12.1997, Criminalite des affaires: une menace pour G Europe/ The European Council, Doc.797 1/12.22. 1997, Economic Crime: a Threat to Europe). Also, in the Report of Canadian Security Intelligence Service (CSIS), of November 1998, the activity of transnational criminal organizations is considered a serious threat to the country's economic security by: crimes in the insurance domain bank fraud, fraud in the payment of fuel taxes, corruption, etc.An important finding of the specialized European bodies is that the phenomenon of economic and financial often called the business crime or white collar crime, is little known and investigated in relation to conventional crime. This fact results on one hand from the appearance of lower social risk, that is the misconception that these facts are less dirty than the ones specific to conventional but especially from the politicization of control activities, which is the essential cause of the weak response to danger, a favoring condition for the development of criminal structures in the contemporary society. Business is perceived by the Council of Europe as a threat to Member States, which is why it has issued the Recommendation no R(81)12 of 25 June 1981, which presents the list of illegal activities confined to this type of crime: Offenses relating to cartel formation; Fraudulent practices and abuses committed by multinational companies; Fraudulent obtaining or misappropriation of funds allotted by the State or international organization; Cybercrime (e.g. data theft, violation of secrecy, manipulation of computer data); Creation of fictitious companies; The forgery of balance sheet and breach of undertaking the obligation of bookkeeping; Frauds which have consequences on commercial status and social capital; Fraud to the detriment of creditors (e. …

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.801
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.058
GPT teacher head0.297
Teacher spread0.239 · 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 designTheoretical or conceptual
Domainnot available
GenreEmpirical

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

Citations1
Published2012
Admission routes1
Has abstractyes

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