We are all in this together: The role of individuals’ social identities in problematic engagement with video games and the internet
Bibliographic record
Abstract
Individuals’ engagement with video games and the internet features both social and potentially pathological aspects. In this research, we draw on the social identity approach and present a novel framework to understand the linkage between these two aspects. In three samples ( N study1 = 304, N study2 = 160, and N study3 = 782) of young Chinese people from two age groups (approximately 20 and 16 years old), we test the associations between relevant social identities and problematic engagement with video games and the internet. Across studies, we demonstrate that individuals’ identification as ‘gamers’ or ‘frequent internet users’ predicts problematic engagement with video games and the internet through stronger perceived social support from such groups. Moreover, we demonstrate that individuals’ identification as ‘students’ (Studies 2–3) is negatively associated with problematic engagement via social support from other students. Finally, in Study 3, we examine the articulation between social support from these three groups and subjective sense of loneliness. Findings indicate that, whereas perceived support from students is negatively associated with loneliness, the association between perceived support from gamers and internet users and loneliness is weaker and positive. Theoretical implications and directions for future research are discussed. Taken together, the studies highlight the importance of considering the social context of individuals’ problematic engagement with technologies, and the role of different group memberships.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".