Automated Author ProfileKil, Woo Yeong
Kil, Woo Yeong
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 1.3 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
Online sexism against female gamers is reportedly common and pervasive, causing serious problems. To help solve these problems, the study identified various predictors of online game sexism, which is hypothesised to predict actual in-game harassment. Different from previous studies, the study approaches the problems from the perspective of perpetrators rather than victims. We proposed a theoretical model that include three groups of predictors: offline sexist beliefs (masculine norms and hostile sexism), game-related factors (perceived territoriality, advancement, and competition), and environmental factors (peer harassment and play time). The model was tested against online survey data collected from a sample of 528 male gamers in South Korea with age range of 14–64 years (M = 34.70, SD = 12.81). The results showed that all the predictors, except competition and play time, were significantly associated with online game sexism, which mediated the relationships between the predictors and online sexual harassment. Perceived territoriality and peer harassment were found to have direct and positive effects on harassment. The findings are expected to contribute to developing more effective measures for preventing the hostility and aggression against female gamers by providing a new and more thorough diagnosis of the underlying causes of the problems.
Authors
- Seo, Young-nam ;
- Oh, Poong ;
- Kil, Woo Yeong
Online sexism against female gamers is reportedly common and pervasive, causing serious problems. To help solve these problems, the study identified various predictors of online game sexism, which is hypothesised to predict actual in-game harassment. Different from previous studies, the study approaches the problems from the perspective of perpetrators rather than victims. We proposed a theoretical model that include three groups of predictors: offline sexist beliefs (masculine norms and hostile sexism), game-related factors (perceived territoriality, advancement, and competition), and environmental factors (peer harassment and play time). The model was tested against online survey data collected from a sample of 528 male gamers in South Korea with age range of 14–64 years (M = 34.70, SD = 12.81). The results showed that all the predictors, except competition and play time, were significantly associated with online game sexism, which mediated the relationships between the predictors and online sexual harassment. Perceived territoriality and peer harassment were found to have direct and positive effects on harassment. The findings are expected to contribute to developing more effective measures for preventing the hostility and aggression against female gamers by providing a new and more thorough diagnosis of the underlying causes of the problems.
Authors
- Seo, Young-nam ;
- Oh, Poong ;
- Kil, Woo Yeong