Bibliographic record
Abstract
Abstract Chinese gangs have a reputation for violence (Daly, 1983; Dannen, 1992). Since the mid-1970s, violence has often erupted in the Chinese communities in North America. In 1977, for instance, five people were killed and eleven were wounded when three assailants opened fire on customers inside a Chinese restaurant in San Francisco’s Chinatown (Ludlow, 1987). In 1982, masked gunmen shot dead three young Chinese and seriously wounded seven others in a bar in New York City’s Chinatown (Blumenthal, 1982). Similar incidents have occurred in Seattle (Em ery, 1990), Boston (Butterfield, 1991), Vancouver (Gould, 1988; Dubro, 1992), and Toronto (Kessel and Hum, 1991; Lavigne, 1991; Moloney, 1991). Most of these incidents were reported to be related to Chinese gangs, and in some, innocent bystanders were wounded or killed. Law enforcement authorities believe that the emergence of Vietnamese and Fujianese gangs, drastic shifts in political alliances among Chinese community organizations, rapid but destabilizing economic expansion in Chinese communities, and the involvement of Chinese gangs in heroin trafficking and in the smuggling of aliens have created an escalation in gang violence over the past few years (U.S. Senate, 1992).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.058 | 0.008 |
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".