Law against Corruption: Outcomes of Corruption Counteraction in Russia
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".