Criminal Offenses in Public Procurement in Albania: A Legal and Institutional Analysis
Bibliographic record
Abstract
The research paper examines the pervasive issue of corruption in public procurement in Albania, highlighting its detrimental impact on the economy and the integrity of state institutions. Through a comprehensive analysis, the study identifies various forms of corruption prevalent in public procurement, including manipulated offers, bribery, and favoritism, and quantifies the economic losses attributed to these illegal practices. The paper delves into the legal framework governing public procurement, particularly focusing on the criminal offenses outlined in the Albanian Criminal Code, including Art. 258, which addresses the violation of equality among participants in tenders and public auctions. The study elucidates the role of the Special Structure against Corruption (SPAK) in combating corruption and the challenges faced in effectively prosecuting these offenses. The findings underscore the need for robust strategies to enhance transparency, accountability, and enforcement mechanisms within the procurement sector. Furthermore, the research proposes several recommendations aimed at improving the legal framework, increasing auditing capabilities, and fostering collaboration between enforcement authorities and civil society to curb corruption in public procurement. Ultimately, the study emphasizes the necessity for ongoing reforms to ensure the integrity and effectiveness of Albania’s public procurement system in alignment with European Union standards.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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".