MétaCan
Menu
Back to cohort
Record W2128980838 · doi:10.1177/2042753014558380

Serious games: video games for good?

2015· article· en· W2128980838 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueE-Learning and Digital Media · 2015
Typearticle
Languageen
FieldPsychology
TopicEducational Games and Gamification
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsVideo game cultureEntertainmentVideo gameMainstreamTurns, rounds and time-keeping systems in gamesGame mechanicsVideo game designEmergent gameplayMetagamingFace (sociological concept)Value (mathematics)MultimediaGame designGame DeveloperPerceptionComputer scienceAdvertisingPsychologySociologyPolitical scienceSocial scienceGame theoryBusinessNon-cooperative game

Abstract

fetched live from OpenAlex

As video games become a ubiquitous part of today's culture internationally, as educators and parents we need to turn our attention to how video games are being understood and used in informal and formal settings. Serious games have developed as a genre of video games marketed for educating youth about a range of world issues. At face value this seems a worthwhile enterprise; however, how is this genre viewed by youth who are immersed in video game culture? This paper explores what can be learned by inviting a group of youth to play and analyze current “serious” games. Key findings include adolescents’ comments on how serious games compare to mainstream entertainment-based games and how world issues are represented in games. Implications from this research suggest that serious game designers need to pay attention to the perceptions and experiences of gamers if video games are going to be developed as instructional tools for youth and children.

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.

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)0.001
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: Empirical
Teacher disagreement score0.816
Threshold uncertainty score0.409

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.030
GPT teacher head0.316
Teacher spread0.286 · 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