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Record W2589664610

Me, Myself, and Interface: The Role of Affordances in Digital Visual Self-Representational Practices

2015· dissertation· en· W2589664610 on OpenAlex
Victoria McArthur

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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueYorkSpace (York University) · 2015
Typedissertation
Languageen
FieldSocial Sciences
TopicDigital Games and Media
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsAffordanceAvatarHuman–computer interactionComputer scienceConstruct (python library)Interface (matter)Artifact (error)Action (physics)Agency (philosophy)MultimediaArtificial intelligenceSociology
DOInot available

Abstract

fetched live from OpenAlex

A growing number of digital games and virtual worlds allow users to create a virtual self, commonly referred to as an ‘avatar.’ Essentially, the avatar is a digital entity which is controlled by the user to attain agency within the virtual world. Avatars are visually customized by users via interfaces, referred to within the body of this work as Character Creation Interfaces (CCIs). 
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\nCCIs are often framed as tools that are utilized by players to create a desired avatar. In other words, the popular approach is one that is anthropocentric in nature and neglects to take into account the ways in which interface affordances - the action possibilities afforded by an artifact - potentially constrain our interactions with them. In my dissertation, I argue that CCIs co-construct avatars with players. I mobilize Actor-Network Theory in order to re-position these interfaces as actors, rather than benign tools in digital-visual self-representational practices. 
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\nIn order to investigate the interface-as-actor I present an analytical framework: the Avatar Affordances Framework, and apply this framework to 20 CCIs in order to systematically study their affordances. In the second phase of this investigation, I present data on two user studies: the first, a within-subjects study investigating self-representational practices in the Massively-Multiplayer-Onlne-Game (MMOG) Rift (n = 39), the other, a between-subjects study of self-representational practices on the Nintendo WiiU console's MiiCreator (n = 24). Results of these two studies are presented alongside analytical data derived from both interfaces via the Avatar Affordances Framework in order to illustrate how interface affordances are negotiated by players. A final study, an autoethnographic chapter, situates myself within the dissertation as both a researcher and user of the technology, addressing how my own experiences with these games, and my own self-representational practices, have come to shape this research.
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\nData from the aforementioned studies was then utilized in order to generate a list of best practices for game developers. To date, such documentation is absent from game design literature. It is my hope that the practices outlined herein help developers make design choices that invite opportunities for identity play without simultaneously creating socially exclusive spaces.

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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.402
Threshold uncertainty score0.695

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.013
GPT teacher head0.306
Teacher spread0.292 · 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