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Record W1514037315 · doi:10.1109/gem.2014.7048100

Gemini redux: Understanding player perception of accumulated context

2014· article· en· W1514037315 on OpenAlexaff
Kevin G. Stanley, Farjana Z. Eishita, Eva Anderson, Regan L. Mandryk

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInnovative Human-Technology Interaction
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsPaceContext (archaeology)Computer sciencePerceptionStyle (visual arts)Human–computer interactionSedentary lifestyleSedentary behaviorMultimediaPhysical activityVideo gamePsychologyApplied psychologyMedicinePhysical medicine and rehabilitation

Abstract

fetched live from OpenAlex

A lack of activity and increased sedentary behavior provide serious health risks in the developed world. Replacing sedentary screen time like watching TV or playing traditional video games has been one proposed method to address this issue through technology. However, active screen time requires that players be in a specific place to exercise, and is therefore less appealing for anti-sedentary behavior measures which must occur regularly throughout the day regardless of the user's location. Linking activity to mobile devices is a more appealing method for combating sedentary behavior. A particularly appealing class of games in this category is the Pervasive Accumulated Context Exergame (PACE) which passively collects activity during the day, and rewards the participant in a later sedentary game. However, it is unclear how the in game rewards are mediated by play style and feedback. In this paper, we examine a highly sophisticated PACE game through a user study to understand how users perceive their activity.

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 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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.730
Threshold uncertainty score0.316

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 communication0.0000.000
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 head0.068
GPT teacher head0.298
Teacher spread0.230 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations5
Published2014
Admission routes1
Has abstractyes

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