Analysis of Drug Trafficking and Corruption Nexus in Economic Cooperation Organization (ECO) Region
Bibliographic record
Abstract
The main objective of Economic Cooperation Organization (ECO) is economic development in its region but directly unproductive profit seeking activities such as drug trafficking is the prominent barrier to reach this goal. All members spend lots of money to fight against drug trafficking, as all of them suffer from drug addiction and drug related problems. The first step to cope with this problem is to identify the factors and incentives that make this region vulnerable for drug trafficking activities and to unearth what makes this region a haven for drug traffickers. A bulk of literature supports the concept that organized criminal organizations are not able to operate when there are not any forms of corruption since they are strongly interrelated. In this paper, we analyze the link between drug trafficking and corruption in ECO region in order to develop ECO strategies to hamper and interrupt these transnational crimes. Corruption has posed major challenges to the efforts taken to control drug and also has seriously damaged the ECO members’ image in international community. One of the practical solutions is the responsibility of ECO organization in implementing rule of law in the region. Undoubtedly, fighting against corruption in ECO region is a joint responsibility of international and intercontinental community and this responsibility requires collective action and cooperation among countries in the region.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".