The Spectacle and Reification of <i>Traumapower</i> in <i>Game of Thrones</i>
Bibliographic record
Abstract
Tortures and executions have been publicly displayed to deter crime, but their staging has also been characterized as spectacle. Public spectacle, and associated technologies, were foundational to the aggregated graphic depiction of Damiens the Regicide sentenced to death by torture. Pain was not considered as the sole purpose, but rather to inflict trauma and cause shame. We see that the sovereign holds power over the body, often considered a vessel to be regulated and managed. In this article, the author explores the events surrounding the walk of atonement conducted by Cersei Lannister in Game of Thrones. This article analyzes how power structures use brutality to cause trauma. The queen is confined to a prison cell where she is psychologically and spiritually tortured by religious fanatics using technologies of private disciplinary power. She is then forced to walk naked through the streets of King’s Landing. As she progresses, her body is pelted with fruit, manure, and bodily fluids, and she endures jeers and curses from the public. A septa (nun) follows her, repeating the word shame eighty-eight times. Her body becomes degraded and objectified. The author’s argument is grounded in a unique reading of disciplinary power, which he has conceptualized as traumapower, defined as the use of public (spectacle) and private (confinement) disciplinary power to inflict physical, psychological, and spiritual harm. In Cersei’s confinement and walk, we see the manifestation of traumapower. This article explores how external power structures are operationalized to assert body control through trauma, thus rendering it docile.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".