Bibliographic record
Abstract
Scholars have long made two claims: ethnic-Chinese criminal entrepreneurs have replaced traditional organised crime in transnational criminal markets including in high-level drug trafficking; there is no discernible structure or commonality (other than the desire for profit) among these informally organised criminals. This study assesses the validity of these assertions by investigating the Big Circle Boys (BCB) in Canada, drawing on qualitative data gathered from the field and from official documents. The BCB is found to be a decentralised network of career criminals comprised of illegal immigrants from Guangzhou, China. Their numbers have been steadily declining since the late 1990s. Among other activities, the BCB are found to be mainly involved in the drug markets, where they have competitively dominated the heroin trade during the 1990s. They do not fit the models of organised criminal group or mafia, but are classified to be a communal business based on Natarajan and Belanger's (1998) typology; this is attributed to their highly connected and collaborative core network comprised of BCB individuals and BCB cell leaders. Other overlapping typological categories found in their cellular network include those of freelance criminal, family business, and corporation. The BCB's violent reputation is not found to be superficial due to documented violent conducts, despite the false perpetuation of this image at times by non-BCB criminals. Internally, the BCB are capable of managing relations and resolving disputes without resorting to violence. The sources of their trust are derived from a combination of achieved and preordained ties, both of which have strong (sub)cultural underpinnings in the Chinese notion of guanxi and the Western concept of social capital. A third BCB generation is not likely to emerge primarily due to the lack of historical and sociocultural conditions which gave rise to the two earlier generations as explained by various classic criminological theories.
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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 | 0.003 | 0.004 |
| Science and technology studies | 0.023 | 0.009 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".