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Record W2944234594

Dynamics of Social Play

2018· dissertation· en· W2944234594 on OpenAlexfundno aff
Ansgar E. Depping

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicOpinion Dynamics and Social Influence
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsLonelinessSocial capitalPopularitySocial psychologyInterpersonal tiesContext (archaeology)FeelingPsychologySocial dynamicsSocial relationSociologySocial science
DOInot available

Abstract

fetched live from OpenAlex

Digital games have become a social medium. Players are often socially motivated to play games and actively seek out games that offer social interactions. Early studies on games such as World of Warcraft demonstrate that players can form meaningful bonds within the game. Catering to this trend, most game titles now include multiplayer experiences in their gameplay. Despite the growing popularity of social elements within play, we still have little empirically-founded guidance on how to effectively design for social experiences. If we want to design for social play, we have to understand what makes games social. What are the properties of play that are responsible for facilitating social ties between players? We address this question by synthesizing the exiting literature on design recommendations for social play into identify overarching properties of play that we think are the most prolific in literature: cooperation and interdependence. We perform two experimental studies demonstrating how games facilitate trust between players and how cooperation and interdependence are crucial properties of social play. Furthermore, we validate our framework in a field study, investigating the experiences within games that predict in-game social capital. We demonstrate that interdependence and toxicity are strongly linked to the social capital our participants experience in their gaming communities. We also illustrate how in-game social capital is negatively associated with feelings of loneliness and positively associated with need satisfaction of relatedness outside of the context of play. Overall, our findings emphasize how strongly the experiences within the game affect the social ties that emerge from play, suggesting that informed design choices are crucial for the success of social games. This dissertation also contributes to the ongoing debate about the effects that in-game relationships have on the player’s mental health—we show a strong positive link between in-game social capital and markers for psychological well-being. It is easy to disregard in-game relationships, as they are fundamentally distinct from the in-person ones we think of as natural. Yet we cannot ignore the emergence of digital games as a social medium. The more we understand the underlying elements of social play, the better we can design games that bring people closer together.

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)0.003
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0040.013
Scholarly communication0.0080.008
Open science0.0010.007
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0170.001

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.

Opus teacher head0.006
GPT teacher head0.290
Teacher spread0.284 · 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 designSimulation or modeling
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

Citations0
Published2018
Admission routes1
Has abstractyes

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