Countering the Use of Leading Sectors of Digital Economy by Organized Crime: European Experience
Notice bibliographique
Résumé
The digital economy on a global scale is developing at a fast pace and acts as an accelerator of innovation, competitiveness and economic growth in the world. Most of the advanced countries of the world, such as the USA, Canada, Japan, and Germany are developing the digital economy and introducing digital technologies in their societies as a strategic goal, which in the future should be the driving force of innovation development, including for the Ukrainian economy. The purpose of the article is to highlight the European experience of preventing and countering organized crime in the digital economy, carrying out an analysis of the novels of modern legislation. The theoretical basis and scientific issues of the chosen scientific direction were considered in the fundamental works of such scholars as: V.M. Butuzov, M.O. Budakov, S.V. Demediuk, V.V. Markov, A.I. Marushchak. Law enforcement agencies should have the tools, methods and experience to combat the criminal misuse of encryption and anonymity methods. To prevent criminals from using encryption and anonymization methods, law enforcement agencies should retrain personnel, and not only employees of units engaged in combating cybercrime, and also have at their disposal the necessary software and hardware systems. In addition, law enforcement officers should be provided with the necessary software tools that allow the use of cyber tools to investigate not only particularly complex, but also any crimes in digital format. Conclusions. Currently, the main task for which the digital economy is aimed is the introduction of digital technologies in industrial production, education, medicine and other fields. It’s common knowledge that the sectors of the economy that use digital technology are developing faster and better. Spheres of human activity, including education, medicine, transport, agriculture, are being modernized thanks to digital technologies, becoming much more efficient and creating new value and quality. Indeed, the continuous development of digital technologies is also one of the reasons for the increase in the scale of the shadow economy, since along with the development of modern technologies, new opportunities for the growth of “digital crime” are emerging. Assessing the impact of the “digital economy” on the national and world economy, as well as inevitably on the entire social sphere, is very important, given the growing problems of the spread of transnational crime in the virtual space, which is also being modernized on a permanent basis. The basis of the development of the digital economy is the blockchain technology, which finds its application in various fields. Describing the state of organized crime in the economic sphere, it is advisable to allocate it in a separate category for the study of crime in the sphere of the “digital economy”. Evaluation of the impact of the digital economy on the national and world economy allows us to state that the continuous modernization of crime remains relevant, which is constantly being improved as part of the active continuous electronicization and digitalization of society. Another factor that should be considered when countering crimes in the “digital economy” is the enormous victimization rate
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,003 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».