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Record W2551675879 · doi:10.1177/1555412016677449

Playing a Better Me: How Players Rehearse Their Ethos via Moral Choices

2016· article· en· W2551675879 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

VenueGames and Culture · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Games and Media
Canadian institutionsConcordia University
Fundersnot available
KeywordsEthosGame mechanicsMetagamingVideo game designAvatarNarrativePresentation (obstetrics)MAGIC (telescope)PsychologySocial psychologySociologyComputer scienceNon-cooperative gameSimultaneous gameHuman–computer interactionGame theoryPolitical scienceArt

Abstract

fetched live from OpenAlex

This article is an exploration of players’ understandings of games that offer moral dilemmas in order to explore player choice in tandem with game mechanics. We investigate how game structures, including the presence of choice, a game’s length, and avatar presentation, push players in particular ways and also how players use those systems for their own ends. We explore how players “rehearse their ethos” through gameplay and how they are continually pushing back against the magic circle. It is based on two-dozen semi-structured interviews with players conducted in 2012. It illustrates that there are no clear-cut answers—game structures, including narratives, character designs, length, or save systems, can push players to act in certain ways that may or may not align with their own beliefs and goals.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.968
Threshold uncertainty score0.257

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 head0.020
GPT teacher head0.262
Teacher spread0.242 · 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