Law against Corruption: Outcomes of Corruption Counteraction in Russia
Notice bibliographique
Résumé
The article is devoted to the scientific analysis of efficiency of legal and organizational measures taken by the state to counteract corruption in the Russian Federation. The authors critically evaluate their effectiveness, pay attention to methodological gaps in choosing means and methods of fighting this scourge. They also substantiate the necessity of rigorous differentiation of legal liability for corruption offences depending on official capacity of the offender and the area of state activity or social life that is encroached by the offender. Examining the genesis of the state’s reactions to the scope and danger of the present problem, it can be said that formal acknowledgment of corruption hazard in society and public service in particular has come after a considerable delay only when this phenomenon took a form that endangered foundations of the society and the state itself and when the global institutions paid attention to a high level of corruption in the Russian Federation. The article studies the impact of law as the most powerful instrument against corruption, the most typical and major drawbacks of legal acts and the degree of their preventive action. The authors emphasize introduction of supplementary restrictions and prohibitions in the civil service system and economy sector. The article draws attention to intensification of criminal repressions for the most dangerous crimes such as bribery, corruption intermediation and others. The conducted analysis of measures taken by the state and assessment of their efficiency by the public consciousness allow us to formulate a scientific hypothesis on the reasons and conditions that have determined poor performance of counteracting corruption. The authors point out some attempts to mobilize the civil society to fight corruption; however the government failed to significantly reduce its level. It is postulated that at present there is a necessity to refine the anti-corruption strategy, to optimize the balance between enforcement measures and stimulation as well as motivation of law-abiding behavior of public servants and others involved in public legal relationships, especially of those related to at-risk group. It is of great significance to intensify state and public control over certain activities such as government and public procurement, budget expenditures, the use of material resources, and others.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».