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Record W2769955409 · doi:10.1177/1354856517743667

Gaming-value and culture-value: Understanding how players account for video game purchases

2017· article· en· W2769955409 on OpenAlexaff
Mark R. Johnson, Yinyi Luo

Bibliographic record

VenueConvergence The International Journal of Research into New Media Technologies · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Games and Media
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsVideo gameValue (mathematics)Game studiesGame DeveloperEntertainmentConceptualizationPurchasingVideo game designOrder (exchange)Game mechanicsIdeologySociologyGame designAdvertisingMarketingComputer scienceBusinessMultimediaMedia studiesPoliticsPolitical science

Abstract

fetched live from OpenAlex

In most writing on video games, whether within or beyond the academy, the availability of gaming media is implicitly taken for granted. However, we propose that the act of video game purchase should be seen as an important aspect of the player–video game relationship. Drawing on original interview data, this work explores two types of video game purchasing that are common in contemporary Western gaming culture – the ‘pre-order’ (paying for a game before its release), and what we term ‘backlog purchasing’ (buying a cheap game unlikely to ever be played). Through Marx and Adorno’s theorizations of value, specifically exchange-value and use-value, we argue that, according to players, the meaningful aspects of those purchases are more than simply obtaining the entertainment value realized through gaming. Instead, different kinds of purchases activities are themselves imbued with varied and powerful values, by both players and the industry. We call these ‘gaming-value’ and ‘culture-value’. Furthermore, drawing on Lewis’ conceptualization of consumer capitalism, this article also traces the ideological root of, and the flow of power beneath, these two particular types of consumption. Through analysing video game purchases, we aim to shed light upon a crucial element of the audience–media relationship, as well as other theoretical issues, most notably adapting and updating Marxist concepts for the purpose of researching modern video games.

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 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)0.003
metaresearch head score (Gemma)0.034
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.696
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.034
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.003
Scholarly communication0.0010.001
Open science0.0030.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.185
GPT teacher head0.439
Teacher spread0.254 · 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 teacher head, not a consensus.

Study designTheoretical or conceptual
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

Citations13
Published2017
Admission routes1
Has abstractyes

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