The Fight Against Corruption in Romania: Struggles to Adhere to the Rule of Law and the (De) Legitimization of the ACA.
Bibliographic record
Abstract
Romania’s desire to establish an efficient anti-corruption agency (ACA) dates back to its zeal for European Union (EU) membership. The former Prime Minister of Romania, Adrian Năstase (Social Democratic Party), played an important role in establishing the anti-corruption agency (ACA), specifically in the context of the country’s EU accession. In a comprehensive sense, Romania’s legislative and administrative framework for fighting corruption is well established. In this thesis, we will look at the actors involved in delegitimizing the ACA’s success, primarily the political opposition and the media, as well as the actors who have helped legitimize the ACA’s efficacy. Furthermore, the political divisiveness between the political opposition and the ACA has caused multi-level conflict within the Romanian government and will provide us with answers to a variety of questions concerning the delegitimization and legitimization of the ACA. Has the political opposition played a role in the negative impacts of the ACA by tarnishing the National Anticorruption Directorate’s (DNA) credibility? Does the political opposition foment limiting the role of effectiveness in the ACA by raising doubts about the agency’s political motivations?
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".