Gaming-value and culture-value: Understanding how players account for video game purchases
Bibliographic record
Abstract
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.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
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".