An analysis of online gaming crime characteristics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it