Business cartels and organised crime: exclusive and inclusive systems of collusion
Bibliographic record
Abstract
In this article, two case studies of large-scale bid rigging in the construction industry in Canada and the Netherlands are analysed to explore why business cartels sometimes do and sometimes do not involve organised crime. By combining concepts from both organised crime and organisational crime, an integrated understanding of the organisation of serious crimes for gain is applied. Across time and space, businesses in the construction industry are known to fix prices, use collusive tendering and divide market shares in illegal cartel agreements. In order to stabilise cartels, participants need to ward off new competitors and prevent cheating within the cartel. The question why we see a system of collusion involving organised crime and violence in Canada as opposed to the Netherlands is answered through analysing two comparable cases. This article finds two systems of bid rigging emerge under different cultural conditions: inclusive and exclusive collusion. The exclusive system makes use of the violent reputation provided by criminal groups and distinguishes from the inclusive system that uses sophisticated administration of mutual claims in shadow bookkeeping.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.010 | 0.042 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".