Media Literacy: Using a Game to Prompt Self-Reflection on Political Truth Biases
Bibliographic record
Abstract
In this paper we examine how games can both capture player biases around truthfulness and facilitate self-reflection on such patterns of biases as a pedagogical approach to media literacy. Our focus is on the study of a game called Fibber, conducted with 344 participants online. The gameplay entails guessing whether statements made by presidential candidates are mostly factual and receiving aggregate feedback on their judgment patterns and potential truth biases. Specifically we sought to answer the questions: 1) how can the game prompt self-reflection in players, 2) what player characteristics are linked to self-reported acts of self-reflection and biases, and 3) how can the study inform future designs of media literacy and self-reflection games? Our results suggest that efforts to promote self-reflection in truth biases – a useful media literacy technique – may be facilitated through aggregation of in-game decisions that can serve as en end-of-game self-reflection prompt. Furthermore, self-reflection on potential political truth biases may be supported by specific in-game behaviors and player characteristics such as gender and political orientation. Future work includes a more experimental comparison of specific game mechanics and qualitative data to better understand the self-reflection process and possible subsequent changes in behavior as a result of self-reported acts of self-reflection.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".