An analysis of online gaming crime characteristics
Bibliographic record
Abstract
Purpose To arouse the public awareness of online gaming‐related crimes and other societal influences so that these problems can be solved through education, laws and appropriate technologies. Design/methodology/approach A total of 613 criminal cases of online gaming crimes that happened in Taiwan during 2002 were gathered and analyzed. They were analyzed for special features then focusing on the tendency for online gaming crime. Related prosecutions, offenders, victims, criminal methods, and so on, were analyzed. Findings According to our analysis of online gaming characteristics in Taiwan, the majority of online gaming crime is theft (73.7 percent) and fraud (20.2 percent). The crime scene is mainly in internet cafés (54.8 percent). Most crimes are committed within the 12:00 to 14:00 time period (11.9 percent). Identity theft (43.4 percent) and social engineering (43.9 percent) are the major criminal means. The offenders (95.8 percent) and victims (87.8 percent) are mainly male and offenders always proceed alone (88.3 percent). The age of offenders is quite low (63.3 percent in the age range of 15‐20), and 8.3 percent of offenders are under 15 years old. The offenders are mostly students (46.7 percent) and the unemployed (24 percent), most of them (81.9 percent) not having criminal records. The type of game giving rise to most of the criminal cases is Lineage Online (93.3 percent). The average value of the online gaming loss is about US$459 and 34.3 percent of criminal loss is between $100 and $300. Research limitations/implications These criminal cases were retrieved from Taiwan in 2002. Some criminal behavior may have been limited to a certain area or a certain period. Practical implications Provides a useful source of information and constructive advice for the public who will sense the seriousness and influence of online gaming crimes. Further, this topic may have implications on e‐commence, e‐services, or web‐based activities beyond gaming. Originality/value Since there is little published research in this area, this paper provides the public with a good and original introduction to a topic of growing importance.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".