Gangs in Asia: China and India
Bibliographic record
Abstract
The problem of gang crimes dates back to the first cities founded thousands of years ago. Its traces can be even discerned in the draconian Hammurabi code of ancient Mesopotamia. To various extents and in many different forms, including muggings, pickpocketing, prostitution and turf wars, it has also plagued ancient Egyptian, Greek and Roman cities, giving ruling classes nightmares and heavily curbing the frequency of their evening walks. Today’s cities across the world continue to be afflicted by them. Although today’s gangs differ, in the increasingly globalized and interconnected world, they also share many characteristics, which have been explored in great depth and with a particular focus on the ‘Western’ culture. This relatively short review will cover the issue of gang crime in the rising superpowers of China and India. Given the scarcity of available data, it will be limited, but it is hoped that it will inspire further focus on these places that tend to be undeservingly ignored in the academic discourse of the West.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".