Young Adult Males’ Online Gaming Experiences and Cyberbullying
Bibliographic record
Abstract
Cyberbullying includes electronic bullying, internet harassment, and cyber aggression. Cyberbullying has known academic, emotional and social consequences. Gender differences in cyberbullying may partially be due to gender socialization. Despite the fact that 71% of Canadian adolescent males play games online, the impact of cyberbullying while online gaming has received minimal attention. To date no research has investigated cyberbullying retrospectively, while engaging in online gaming. The purpose of this study is to examine: (1) how young adult males describe and understand their adolescent experiences of online gaming? (2) how do young adults males describe, understand, interpret and explain the role that friendships played in their previous adolescent experiences of online gaming? And (3) how do young male adults frame “normal” online gaming culture and how do they describe and understand cyberbullying? This study takes a basic qualitative research approach involving semi-structured focus groups and thematic data analysis. Each focus group will consist of 5-8 young adult males (18-25) who self-identify as online gamers. Although data collection is currently on-going, the study should be complete by the beginning of 2019. A deeper understanding of how adolescents experience cyberbullying through online gaming will us develop better ways of addressing cyberbullying.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".