Transmediating difference: Fictional filter bubbles and transmedia storytelling
Bibliographic record
Abstract
Transmedia storyworlds often stem from a blockbuster “anchor property” that connects numerous extensions in multiple media forms. Consequently, transmedia can potentially diversify the media industry’s narratives since each medium may follow a different character whose perspective reinterprets the storyworld’s central themes and events. However, this article argues that a narrative strategy has emerged for “transmediating difference,” wherein politically contested storylines, LGBTQ characters, and the perspectives of women and people of color are sectioned off in low-budget transmedia extensions while blockbuster narratives remain primarily the domain of straight, able-bodied, white male protagonists. This story structure reveals the resiliency of industry assumptions about marketability while also isolating the experience of transmedia audiences, allowing companies to profit from inclusive and sanitized versions of the same narrative world. Like algorithmic “filter bubbles,” transmediating difference undermines cultural pluralism, and ushers in a paradoxical new form of invisibility despite increasingly diverse representation.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".