Affective Proximity: Tracing Jaime Lannister’s Moral Progression in HBO’s <i>Game of Thrones</i>
Bibliographic record
Abstract
Jaime Lannister is one of the most intriguing characters in HBO’s Game of Thrones due to his important role in the show’s chief area of conflict, King’s Landing, as well as his incestuous relationship with his sister and, because of this relationship, her strong influence on his character. What makes the dynamic of Jaime and Cersei’s relationship so compelling is the manner in which Cersei’s influence on Jaime seems to grow weaker or stronger depending on where he is travelling in Westeros, how far away he is from her, both physically and emotionally, and which characters he spends time with during these travels. An effective means of analyzing Jaime’s moral development in relation to his relationship with his sister is by way of affect theory—specifically the affect of proximity, which authors such as Sara Ahmed and Lauren Berlant discuss in The Promise of Happiness (2010) and Cruel Optimism (2011), respectively. By mapping out Jaime’s travels during the series, as well as through close reading of several key moments of his journey, I argue that Jaime’s ever-changing “affective proximity” to Cersei strongly affects his moral character, ultimately resulting in his heading to Winterfell to help fight the last war.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".