The Impact of Gaming on Fear of Missing Out: The Case of Bahcesehir University E-Sports Team
Bibliographic record
Abstract
The presents study sought the impact of gaming on the fear of missing out on social media and compared these variables by the participants’ demographic characteristics. A total of 94 e-Sports players, 9 females (9.6%) and 85 males (90.4%), participated in the research. The data were collected using a demographic information form, the Digital Game Addiction Scale, and the Fear of Missing out Scale. Demographic characteristics of the participants were shown as frequencies and percentages. The Kolmogorov-Smirnov and Shapiro-Wilks tests revealed the data showed a normal distribution. Therefore, an independent samples t-test, a one-way analysis of variance (ANOVA), and a linear regression analysis were utilized to examine the relationships between the variables. All analyses were performed on SPSS 20.0 at a 95% confidence level. The findings suggested that the young engaging in gaming for a long time may have several problems such as a decline in academic achievement, normalization of violent behaviors in games, decrease in communication with family members, disruptions in social relations, and deteriorated vision.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".