Bibliographic record
Abstract
By allowing viewers to interact with stories, interactive films have revolutionized how viewers engage with social issues and events. The emergence of immersive technologies and interactive storytelling has transformed traditional storytelling forms and methods. These technological advances have rendered interactive storytelling more complex by challenging users to reexamine understandings of the mediated world. Consequently, basic media literacy skills are necessary to benefit from these projects dealing with refugees stories.
 Although interactive storytelling creates new forms of social engagement, the question about which social issues or subjects are most effectively addressed through interactive narratives remains. How do refugee stories and discourses contribute to interactive engagement? This essay analyzes a series of interactive projects focusing on refugees' storytelling, politics, and aesthetics in order to examine their socio-political engagement and their unique attributes. Refugee stories hold a strong emotional appeal, positioning them as ideal for interactive narratives. How do unique refugee experiences of displacement and disarray engage creators and viewers/users/players?
 This essay argues that refugee storytelling interactivity contributes significantly to viewers' engagement with social issues and characters. While creating empathy and responsibility, these projects allow users to connect with people far removed from their own lived experiences. Characters with well-known and reliable stories make users feel closer in the position of moral concern to their situation and can instigate political accountability for their future. In interactive narratives, refugee characters redefine agency and storytelling. They offer the ability to recreate history, engender a change in the world, or revise a topic with a sense of justice toward dependable and trustworthy characters.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".