Who Are You? Cartel Detection Using Unlabeled Data
Bibliographic record
Abstract
We propose a data-driven machine learning approach to flag bid-rigging cartels in the Brazilian road maintenance sector. First, we apply a clustering algorithm to group the tenders based on their attributes. Second, we use the labels created by the clustering algorithm as a target variable to predict them using a classifier. We rank the predictors according to their relevance to decrease the number of false positive (detect cartel when it does not exist) and false negative (do not detect cartel when it does exist) predictions. Our results shed light on the need to use a range of predictors to recognize the vast profile of strategies practiced by bid-rigging cartels, such as misleading competitive dynamics, bid combination, and cover bidding behavior. Our method can improve cartels' deterrence in different economic sectors, especially when labeled data are not available. In a controlled environment with a simulated dataset, the overall average accuracy of the algorithm is 99.33%. In a real-world cartel case with a labeled dataset, the overall average accuracy is 80.25%. When applied to the road maintenance dataset, our model identified a group containing 273 (31% of the total) suspicious tenders. We conclude by offering a policy prescription discussion for antitrust authorities.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".