From Innocence to Experience: On the Significance of Sansa Stark's Costumes in HBO's Game of Thrones
Bibliographic record
Abstract
HBO’s Game of Thrones is widely regarded as one of the most ambitious and sophisticated series in the history of television. Based upon the enormously popular historical/fantasy novels of George R.R. Martin, the series is admired for its epic scale and for its elaborate world-building. HBO’s Game of Thrones is praised for its spectacular set designs and its skilful blending of CGI and location shots to create an instantly recognizable visual style. From the opening credit sequence to the richly textured and nuanced representations of the fictional worlds of Essos, Pentos and Westeros, the mise-en-scene in Game of Thrones has played a significant role in winning the support of die-hard fans and scholars alike. To date, there have been more than a half-dozen monographs and/or collections of critical essays published on the landmark television series. Our essay aims to add to this commentary by exploring the significance of costume design in Game of Thrones . Inherently, costume design serves multiple functions. At one level, they help lend a degree of realism and historical accuracy to the characters and settings. They also help to define specific characters both as individuals and as members of specific social classes or groups. However, costume can also visually foreground narrative arcs and themes. This is the case for Sansa Stark. Through a dramatic transformation in costuming, the series showcases her character’s growth from innocence to experience.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".