Collusion in Repeated Game: The Issue and the Suggested Combating Strategy for International Cartel
Bibliographic record
Abstract
The international cartel does not positively influence the world’s economy. It is inefficient to allocate resources by setting a very high price. This paper emphasizes one of the most famous examples of international cartels in the real world - OPEC and a possible international cartel in the future - GECF. Finding out the damages brought by OPEC and GECF, and the predicted damages brought by GECF in the future, highlight the negative impact it brings to call for governments’ attention on the cartel. Then, making some comparisons of OPEC and GECF will help to make different target strategies for them. Also, this paper provides some suggestions to combat international cartels, like standardizing the cartel’s definition and constructing a global jurisdiction organization to solve the cartel problems. Although there is a long way to go in combating international cartels, some actions are needed urgently to witness the increasing power of international cartels.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".