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Record W3204888765 · doi:10.1145/3474711

Players' Stories and Secrets in Animal Crossing

2021· article· en· W3204888765 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

VenueProceedings of the ACM on Human-Computer Interaction · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Games and Media
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsNarrativeSocial mediaGame designCoronavirus disease 2019 (COVID-19)Internet privacyPsychologySocial psychologySociologyComputer scienceMultimediaWorld Wide WebArt

Abstract

fetched live from OpenAlex

Animal Crossing is an online multiplayer game that supports social communication and collaboration. Its recent version, New Horizons, is immensely popular having sold over 32 million copies worldwide, with many players attracted to the opportunities it provides to remotely socialize during the COVID-19 pandemic. To understand players' increased positive emotions and social interactions, we surveyed 119 of them betweenMay and December 2020 and conducted remote interviews with 25 respondents. We identified the social dynamics among players and with non-player characters (NPCs), and analyzed how positive social interactions were facilitated under player-generated narratives and game-determined narratives. Based on our empirical analyses, we have extended our understanding of how to create positive, safe, and friendly interactions: (1)the design of mood-improving game worlds with flexible game tasks, (2) implementation of game-determined activities with social implications, (3) provision of player rewards to reinforce their social interactions, and (4)creation of opportunities to integrate NPCs' game-determined narratives into player-generated narratives.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.424
Threshold uncertainty score0.398

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 communication0.0000.001
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 head0.056
GPT teacher head0.359
Teacher spread0.303 · 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