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Record W2898272590 · doi:10.1145/3242671.3242702

Designing for Friendship

2018· article· en· W2898272590 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldArts and Humanities
TopicMedia Influence and Health
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsLonelinessSocial capitalFriendshipFeelingSocial psychologyPsychologyAffect (linguistics)Sociology

Abstract

fetched live from OpenAlex

Players are increasingly viewing games as a social medium to form and enact friendships; however, we currently have little empirically-informed understanding of how to design games that satisfy the social needs of players. We investigate how in-game friendships develop, and how they affect well-being. We deployed an online survey (N= 234) measuring the properties of games and social capital that participants experience within their gaming community, alongside indicators of the social aspects of their psychological wellbeing (loneliness, need satisfaction of relatedness). First, our findings highlight two strong predictors of in-game social capital: interdependence and toxicity, whereas cooperation appears to be less crucial than common wisdom suggests. Second, we demonstrate how in-game social capital is associated with reduced feelings of loneliness and increased satisfaction of relatedness. Our findings suggest that social capital in games is strongly and positively related to players' psychological well-being. The present study informs both the design of social games as well as our theoretical understanding of in-game relationships.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.950
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.000

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 head0.155
GPT teacher head0.323
Teacher spread0.168 · 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

Quick stats

Citations63
Published2018
Admission routes2
Has abstractyes

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