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Record W2009912207 · doi:10.1145/2531602.2531661

How players value their characters in world of warcraft

2014· article· en· W2009912207 on OpenAlexafffund
Ian J. Livingston, Carl Gutwin, Regan L. Mandryk, Max V. Birk

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Games and Media
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsValue (mathematics)Computer scienceCreativityRepresentation (politics)Human–computer interactionMultimediaPsychologySocial psychology

Abstract

fetched live from OpenAlex

Characters in games such as World of Warcraft allow players to act in the game world and to interact with others. Game characters and avatars are a mediated form of self-representation for the player, but some research suggests that players also view characters in other ways that have to do with the kinds of value that the characters provide. To better understand the ways that players value their characters in an online environment, we carried out a semi-structured interview study of twenty World of Warcraft players. From our data we identify ten kinds of value that characters can provide -- including utility, investment, communication, memory, enjoyment, and representations of relationships, as well as value as an opportunity for experience, creativity, sociability, and self-expression. The analytical lens of value provides a new understanding of the ways that players appreciate characters in online multi-user worlds. Our results can help developers understand and enhance an element of multi-player games that contributes greatly to player experience and satisfaction.

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 score0.974
Threshold uncertainty score0.315

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 head0.017
GPT teacher head0.252
Teacher spread0.235 · 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

Citations46
Published2014
Admission routes2
Has abstractyes

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