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Record W4396832049 · doi:10.1145/3613904.3642273

Play Across Boundaries: Exploring Cross-Cultural Maldaimonic Game Experiences

2024· preprint· en· W4396832049 on OpenAlexaff
Katie Seaborn, Satoru Iseya, Shun Hidaka, Sota Kobuki, Shruti Chandra

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldArts and Humanities
TopicMedia Influence and Health
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsConstruct (python library)Experiential learningPsychologySet (abstract data type)Social psychologyDivergence (linguistics)Qualitative researchCross-culturalSociologyCognitive psychologyComputer scienceSocial scienceLinguistics

Abstract

fetched live from OpenAlex

Maldaimonic game experiences occur when people engage in personally fulfilling play through egocentric, destructive, and/or exploitative acts. Initial qualitative work verified this orientation and experiential construct for English-speaking Westerners. In this comparative mixed methods study, we explored whether and how maldaimonic game experiences and orientations play out in Japan, an Eastern gaming capital that may have cultural values incongruous with the Western philosophical basis underlying maldaimonia. We present findings anchored to the initial frameworks on maldaimonia in game experiences that show little divergence between the Japanese and US cohorts. We also extend the qualitative findings with quantitative measures on affect, player experience, and the related constructs of hedonia and eudaimonia. We confirm this novel construct for Japan and set the stage for scale development.

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)0.002
metaresearch head score (Gemma)0.004
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 score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0030.004
Scholarly communication0.0040.002
Open science0.0010.005
Research integrity0.0010.001
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 head0.191
GPT teacher head0.394
Teacher spread0.203 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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