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Record W3107616170 · doi:10.3390/mti4040089

The Response to Impactful Interactivity on Spectators’ Engagement in a Digital Game

2020· article· en· W3107616170 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueMultimodal Technologies and Interaction · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicMedia Influence and Health
Canadian institutionsHEC Montréal
FundersMitacs
KeywordsInteractivityGame designPsychologyGame mechanicsGame playVideo gameMultimediaSocial psychologyAdvertisingComputer scienceBusiness

Abstract

fetched live from OpenAlex

As gaming spectatorship has become a worldwide phenomenon, keeping the spectator in mind while designing games is becoming more important. Here, we explore the factors that influence spectators’ engagement. Through the use of GRiD Crowd, a game akin to life-size Pong, different levels of spectator influence on the game were tested and their impact on engagement via arousal measures were analyzed. Spectator influence on the game was accomplished via smartphone, where 78 participants put in different audience compositions (alongside friends or strangers) were tested. We found that when the spectators had an impact on the game, higher levels of emotional arousal were recorded, which generated an increase in engagement. These results provide a suggestion of design that could be used by game designers who wish to engage their spectatorship, a segment of their target market that is becoming impossible to ignore.

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 score0.860
Threshold uncertainty score0.341

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 head0.062
GPT teacher head0.312
Teacher spread0.250 · 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