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Record W7072054381

Video Games as Tools for Non-State Cultural Diplomacy: A Case Study of The Video Game Never Alone

2021· dissertation· en· W7072054381 on OpenAlexaboutno aff

Bibliographic record

VenueCU Scholar (University of Colorado Boulder) · 2021
Typedissertation
Languageen
FieldSocial Sciences
TopicPublic Relations and Crisis Communication
Canadian institutionsnot available
Fundersnot available
KeywordsVideo gameDiplomacyLegitimacyAction (physics)Video game cultureBridge (graph theory)Cultural diversityProcess (computing)Globalization
DOInot available

Abstract

fetched live from OpenAlex

With rapidly changing digital platforms and the impact of globalization on cultural diplomacy that has provoked the increasing mediatization of the field, non-state actors are now able to expand networks and gain legitimacy by addressing new publics using digital spaces. While the research about the use of social media as a vehicle for cultural diplomacy continues to increase, little work has been done reflecting on the potential of another digital medium: video games. This study intends to fill that gap by assessing video games&rsquo; potential to serve as tools for cultural engagement in cultural diplomacy programming. Using the video game <em>Never Alone</em> as a case study, this dissertation explores the opportunity to provide a digital third space where imagined contact can occur to foster cultural understanding. <em>Never Alone</em> is a puzzle platformer video game developed in collaboration with the I&ntilde;upiaq Alaska Natives to showcase their culture. The game&rsquo;s inclusive development process also works to examine virtual third spaces as a site for decolonial action at the microlevel regarding the I&ntilde;upiaq and the video game industry and at the macrolevel regarding Inuit diplomacies. This research attempts to critically explore the possibility for non-state cultural diplomacy initiatives using video games as tools to shape perceptions about the world.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.001
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 head0.031
GPT teacher head0.328
Teacher spread0.296 · 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.

Study designQualitative
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

Citations0
Published2021
Admission routes1
Has abstractyes

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