Online game addiction among youth in China: How do different stakeholders look at online game addiction among Chinese youth?
Bibliographic record
Abstract
In the last few decades the internet has become an indispensable part of people’s lives. Especially the younger generations have a broad knowledge of how digital devices work and the advantages and conveniences of using the internet. According to Golub and Lingley, the Chinese government has always viewed the internet with ambivalence, because on the one hand it is a sign of modernity, but on the other hand it provides access to a different world view, different opinions, and additional information, which might not conform to the government’s view (2008). However, the number of internet users in mainland China has increased from 620.000 in 1998 to 649 million people at the end of 2014, it is the world’s fastest–growing online population (Stewart 2010; CNNIC 2015). About 55% of those users are below 30 years old. In the same year, China had a total of 366 million online gamers and the largest game population in the world (CNNIC 2015).\nOnline gaming has become a popular way for children, adolescents and adults alike to spend their free or not so free hours. However, with the gaining popularity of online and offline gaming, it often replaces other more social activities, like playing sports or real-life socializing. Gaming addiction also has become a very serious issue, which has a profound impact on society. Since 2002, there have been many cases of aggression and bizarre behavior caused by gaming addiction. Golub and Lingley (2008) mention a few in their article, such as an obese man dying after a marathon gaming session, and a 13-year old jumping of a building after playing Warcraft hoping to “join the heroes of the game” (62; Xinhua News Agency 2006). Although, in a lot of countries, there is still an ongoing debate about whether gaming addiction is an addiction at all, China was one of the first to label problematic online gaming as a clinical disorder and is together with South Korea, the Netherlands, Canada and the United States, one of the few countries that has treatment centers for internet addiction, the first being opened in 2004 (CCTV International 2009).\nIn my thesis I will discuss different stakeholders and how they look at the phenomenon of online game addiction among Chinese youth. I will use Foucault’s theory on power relations as a supporting framework for my research (Foucault 1981). I define Chinese youth as the age group between 12 and 30, but with a focus on high school and higher education students. I define online gaming as using an internet connection while gaming, often on a computer, instead of offline gaming on a console, and focus mainly on Massively Multiplayer Online Games (MMO) as they are believed to be the most addictive online games and they are the most widespread in China.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".