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Record W2134844607 · doi:10.26503/dl.v2013i1.672

Constructing the Ideal EVE Online Player

2014· article· en· W2134844607 on OpenAlexaff
Kelly Bergstrom, Marcus Carter, Darryl Woodford, Christopher Paul

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Games and Media
Canadian institutionsYork University
Fundersnot available
KeywordsSandbox (software development)Computer scienceReputationIdeal (ethics)Computer securityMultimediaSociologyEpistemology

Abstract

fetched live from OpenAlex

EVE Online, released in 2003 by CCP Games, is a space-themed Massively Multiplayer Online Game (MMOG). This sandbox style MMOG has a reputation for being a difficult game with a punishing learning curve that is fairly impenetrable to new players. This has led to the widely held belief among the larger MMOG community that “EVE players are different”, as only a very particular type of player would be dedicated to learning how to play a game this challenging. Taking a critical approach to the claim that “EVE players are different”, this paper complicates the idea that only a certain type of player capable of playing the most hardcore of games will be attracted to this particular MMOG. Instead, we argue that EVE’s “exceptionalism” is actually the result of conscious design decisions on the part of CCP games, which in turn compel particular behaviours that are continually reinforced as the norm by the game’s relatively homogenous player community.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.979
Threshold uncertainty score0.247

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.024
GPT teacher head0.304
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 teacher head, 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

Citations17
Published2014
Admission routes1
Has abstractyes

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