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Record W2313343825 · doi:10.1177/1555412014565507

Alienated Playbour

2015· article· en· W2313343825 on OpenAlexaff
Nicholas Taylor, Kelly Bergstrom, Jennifer Jenson, Suzanne de Castell

Bibliographic record

VenueGames and Culture · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Games and Media
Canadian institutionsOntario Tech UniversityYork University
Fundersnot available
KeywordsSituatedGame DeveloperIdeologyGame designVideo gameGame art designGame mechanicsPerspective (graphical)Game studiesMetagamingSociologyVideo game designExtension (predicate logic)Non-cooperative gamePublic relationsPolitical scienceEconomicsGame theoryComputer scienceMultimediaMedia studiesSimultaneous gamePoliticsMicroeconomics

Abstract

fetched live from OpenAlex

This article explores the play practices of EVE Online industrialists: those primarily responsible for generating the materials and equipment that drive the game’s robust economy. Applying the concept of “immaterial labor” to this underattended aspect of the EVE community, we consider the range of communicative and informational artifacts and activities industrialists enact in support of their involvement in the game—work that happens both in game and crucially outside of it. Moving past the increasingly anachronistic distinctions between digitally mediated labor and leisure, in game and out of game, we examine the relations of production in which these players are situated: to other EVE players, in-game corporations, the game’s developer, and the broader digital economy. Seen from this perspective, we consider the extent to which EVE both ideologically and economically supports the extension of capital into increasing aspects of our everyday lives—a “game” in which many play, but few win.

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)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.022
Scholarly communication0.0080.005
Open science0.0010.013
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0200.003

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 head0.290
Teacher spread0.265 · 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 designNot applicable
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

Citations47
Published2015
Admission routes1
Has abstractyes

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