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Record W1580884378

An Ethnographic Study of Collaboration in a Game Development Team

2009· article· en· W1580884378 on OpenAlexaff
Quang Tran Minh, Robert Biddle

Bibliographic record

VenueOpen Research Online (The Open University) · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Games and Media
Canadian institutionsCarleton University
Fundersnot available
KeywordsEthnographyGame design documentGame designGame DeveloperKnowledge managementThematic analysisConversationField (mathematics)Process (computing)New product developmentInterpersonal communicationVideo game developmentProduct (mathematics)Game art designComputer scienceSociologyPsychologyQualitative researchMultimediaMarketingSocial psychologyBusinessCommunicationSocial science
DOInot available

Abstract

fetched live from OpenAlex

This paper presents an ethnographically-informed study of the practices at a game company that develops serious games for business training. Games research focuses mainly on the game product, paying little attention to the design and development process. This study attempts to address this gap. We examined the day-to-day activity of a team responsible for designing and developing game content. Our data collection methods were informed by ethnography; they include field observations, contextual interviews and audio recordings. A thematic analysis was performed on transcripts of naturally occurring conversation within the team. The results were triangulated with field notes and interview data. The result is a description of collaborative activity within a game development team, and an interpretation of how the socio-technical environment supported the game development process. This study suggests innovative game design can be supported by creating a culture of collaboration, but innovation from teams is largely dependent on the quality of the interpersonal relationships.

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)0.008
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.022
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.002
Science and technology studies0.0100.006
Scholarly communication0.0050.005
Open science0.0010.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.144
GPT teacher head0.470
Teacher spread0.326 · 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 designQualitative
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

Citations11
Published2009
Admission routes1
Has abstractyes

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