Reviewing the Strategies of Dealing with Corruption in the Europe Union Countries with Regulatory System of the Group of States against Corruption (GRECO)
Bibliographic record
Abstract
<p>This paper attempts to examine the provisions of the Convention on Combating Bribery of Foreign Public Officials in International Business Contracts, Europe Union Convention on Combating Corruption and also the analysis of the actions of the Council of Europe in implementation of the convention on criminal law, Additional Protocol to the Criminal Law Convention on Corruption and the Convention relating to anti-corruption civil law, in order to describes different ways to combat corruption and provide a suitable solution to combat corruption in the country.</p><p>Reviewing the standards and regulations in EU in the executive and legislative sector and inspection system and the Member States known as GRECO, it is concluded that the EU has been trying to implement effective measures by increasing the thematic range and personal inclusion of provisions to combat corruption and strengthen the regulatory system of GRECO; and the success of the EU in this field is largely due to these actions so that it is known as the leader in the fight against corruption according to reports and statistics compiled by Transparency International Organization.</p>The Europe Union has achieved such success considering appropriate social fields among member states and national and international regulatory systems. Accordingly, generalization of EU pattern to other regional or global institutions involved in the fight against corruption seems somewhat unlikely, but the experience of EU in this area can be partially passed on to others. Meanwhile, the regulatory and coping patterns of the EU and its member states can also be used for the Islamic Republic of Iran.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".