Focal and peripheral consumption journeys across virtual and physical realities: A study of online gaming
Bibliographic record
Abstract
This research investigates how families negotiate the intensification of competitive gaming, focusing on both focal consumers (gamers) and peripheral consumers (parents, siblings, or partners). Drawing on ethnographic, netnographic, and interview data, the study addresses how a virtual practice evolves as gamers become more committed and how its virtual and physical elements affect not only gamers’ experiences but also those of non-gamers. The authors have found that the negotiation of the practice follows an iterative process of evaluation, circumscription, and reconfiguration, leading to shifts in household norms, noise management, and spatial arrangements. As gaming transitions into a more immersive, virtual phase, its partial visibility can alienate non-gamers or heighten tensions regarding autonomy and shared routines. This study also shows that misalignments between material arrangements, doings, and meanings underpin many of the conflicts—yet they can be mitigated through supportive design choices, offline–online integration, and third-party mediation. This research highlights the pivotal role that peripheral consumers play in shaping emerging digital consumption practices. It concludes with practical and policy-level implications, offering strategies that product developers, community organizers, and educators can implement to foster household harmony and preserve gaming’s communal potential.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".