“They All Lived Happily Ever After. Obviously.”: Realism and Utopia in Game of Thrones-Based Alternate Universe Fairy Tale Fan Fiction
Bibliographic record
Abstract
Fan fiction alternate universe stories (AUs) that combine Game of Thrones characters and settings with fairy tale elements construct a dialogue between realism and wonder. Realism performs a number of functions in various genres, but becomes a particularly tricky concept to tie down in fantasy. Deployments of realism in “quality TV” series like Game of Thrones often reinforce social stigmatization of feminine genres like the romance, melodrama, and fairy tale. The happily-ever-after ending receives significant feminist criticism partly because it falls within a larger framework of utopian politics and poetics, which are frequently accused of essentialism and authoritarianism. However, because fan fiction cultures place all stories in dialogue with numerous other equally plausible versions, the fairy tale happy ending can serve unexpected purposes. By examining several case studies in fairy tale AU fan fiction based on Game of Thrones characters, situations, and settings, this paper demonstrates the genre’s ability to construct surprising critiques of real social and historical situations through strategic deployment of impossible wishes made manifest through the magic of fan creativity.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.017 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".