Bibliographic record
Abstract
Employed almost synonymously with disinformation and misinformation, fake news refers to the increasing discourse of misconfigured news and information being shared online which has prompted global concern. Calls for digital literacy have come from researchers, governments, and public interest groups who developing an array of resources for the public. Games are one intervention. This article explores what it refers to as ‘fake news games’. Not focusing on a specific game genre, it considers video games that discuss or present fake news as central to their play or design. This paper evaluates how fake news is being presented in games and asks how the concept understood across these games. By analyzing the content, skills, and goals in these games, it situates fake news alongside digital literacy skills to see how the term is being re-framed by the medium of games. Twenty-two games were studied from a larger sample collected in late 2020. Through play analysis of twenty-two fake news video games collected in 2020 this paper provides an overview of game’s that discuss fake news. Games were play-tested and recorded to see the range of content, skills and central themes that were invoked in these games. These led to findings discussing the design, core premise, and general discourse around fake news that was promoted through play. The findings in this article offer value for future directions of discussion and game design focused on fake news. By pointing to gaps and differences in games in the field, this article offers potential information for designers while also highlighting how fake news is re-framed by these games. It emphasizes which points of interest around fake news are commonly being brought up, and points to future design and implementation considerations for scholars and designers.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".