Terror Financing: Back in Business. A Case study of the Democratic Republic of Congo (ex-Zaire)
Bibliographic record
Abstract
Conflict diamonds are often used in lieu of currency in arms deals, money laundering, and other criminal activities; they are easily concealed and transported and virtually untraceable to their original source. The United Nations (U.N) General Assembly defines conflict diamonds as “rough diamonds used by rebel movements to finance their military activities, including attempts to undermine or overthrow legitimate governments” . The Kimberly Process Certification Scheme (KPCS) is the international response to the destabilizing effect of “rough” or conflict diamonds in the global search for sustainable peace and development in Sierra Leone, Angola, The Democratic Republic of Congo (DRC), and other parts of Africa. It intends to eliminate trade in conflict and stolen diamonds with a view of cutting off the flow of much needed diamonds. These are used by rebels to purchase arms and ammunitions. The purpose is to eliminate or reduce armed conflicts in the affected states. The case will be made of the loophole in the KPCS that can further weaken the global security and destabilize Africa. Though the KPCS is sound, there is a need to improve its essence. Stakeholders and the United Nations must develop a more comprehensive legal regime to strengthen the process by creating a regulatory body of diamond inspectors similar to the International Atomic Energy Agency. If nothing is done, we can easily envision or detect the possibility of money laundering, which could aid in the financing of terrorist organizations. A grim picture of that reality is almost visible in the DRC. Without democracy and strong legal institutions, the Democratic Republic of Congo could be a refuge for terror financing activities. What is stopping terror organizations from flourishing in this chaotic landscape? Nothing. The authority of the central government is concentrated in the hands of a few who are motivated by self-interest. Vast natural resources, such as coltan, diamonds, copper, gold and Uranium, are found throughout the country in regions where government control is weak or non-existent. Therefore, lawlessness consisting of a chaotic environment looms large over the country, inviting extremist organizations and their operatives to easily blend in with the diasporas of their respective communities and mastermind their next strikes. The downfall of the transitional government can be provoked by socio-economic problems. The living condition of the average Congolese has become unbearable. The popular voices in the country are continually grumbling. As this grumbling grows, it could crystallize and induce an uncontrollable reflex among the population to revolt. The apparent calm is misleading; the situation is a time bomb. If nothing is done, history will repeat itself and the DRC will become a balkanized country with all its horrible implications.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".