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Record W2085141431 · doi:10.1177/1555412009343572

Discipline and Dragon Kill Points in the Online Power Game

2009· article· en· W2085141431 on OpenAlexaff
Mark E. Silverman, Bart Simon

Bibliographic record

VenueGames and Culture · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Games and Media
Canadian institutionsConcordia UniversityUniversity of Calgary
Fundersnot available
KeywordsSubjectivityArgument (complex analysis)Power (physics)PanopticonIdeal (ethics)DisciplineSociologyEpistemologyControl (management)Game studiesRationalismPolitical scienceComputer scienceMedia studiesSocial sciencePhilosophyPoliticsLawArtificial intelligence

Abstract

fetched live from OpenAlex

This article discusses the origins and development of the player-innovated dragon kill point (DKP) system as an example for thinking about Foucauldian conceptions of disciplinary power and the production of gamer subjectivity in the contexts of massively multiplayer online game (MMOG) power gaming. The argument considers the generalized hyperrationalism of DKP-based gaming as both an ideal digital form of panoptic control as well as a kind of ironic form of play with the limits of the possibility of control within digital culture.

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)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.021
Scholarly communication0.0080.007
Open science0.0010.006
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0060.000

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 head0.009
GPT teacher head0.290
Teacher spread0.280 · 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 designObservational
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

Citations12
Published2009
Admission routes1
Has abstractyes

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