Video Games as Tools for Non-State Cultural Diplomacy: A Case Study of The Video Game Never Alone
Bibliographic record
Abstract
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’ potential to serve as tools for cultural engagement in cultural diplomacy programming. Using the video game Never Alone as a case study, this dissertation explores the opportunity to provide a digital third space where imagined contact can occur to foster cultural understanding. Never Alone is a puzzle platformer video game developed in collaboration with the Iñupiaq Alaska Natives to showcase their culture. The game’s inclusive development process also works to examine virtual third spaces as a site for decolonial action at the microlevel regarding the Iñupiaq 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.008 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".