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Record W2404348203 · doi:10.1177/1555412015601757

Older Adults’ Social Interactions in Massively Multiplayer Online Role-Playing Games (MMORPGs)

2015· article· en· W2404348203 on OpenAlexaff
Fan Zhang, David Kaufman

Bibliographic record

VenueGames and Culture · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicImpact of Technology on Adolescents
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsCasualPsychologyClubSocial relationSocial psychology

Abstract

fetched live from OpenAlex

The purpose of this study was to investigate older adults’ social interactions in massively multiplayer online role-playing games (MMORPGs). An online survey was developed and published on eight World of Warcraft (WoW) player forums to gather information about older gamers’ demographic characteristics, play patterns, social interactions in the game, and challenges facing older adults while playing WoW. Results indicate that as for their younger counterparts, older adults’ social interactions in MMORPGs are motivated by social, achievement, and immersion factors; can take place on several different levels; and can be casual or intimate. As in previous research, respondents in this study reported that playing MMORPGs offered older adults opportunities to sustain off-line relationships with family and real-life friends and to build meaningful and supportive relationships with game friends. This study also demonstrated that MMORPGs have the potential to function as a “third place” for older adults to socialize and be entertained as in a real-world club or coffee shop.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.025
GPT teacher head0.327
Teacher spread0.301 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations43
Published2015
Admission routes1
Has abstractyes

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