Chivalry and Knighthood in the Past and Present; Contrasting “Gawain” and Fire Emblem Three Houses
Bibliographic record
Abstract
In my paper, I delve into the socio-political dimensions of knighthood and chivalry during the Medieval era of Europe through a comparison between the Medieval English poem, “Sir Gawain and the Green Knight,” and the video game, Fire Emblem Three Houses, published in 2019 by Nintendo. Within both texts, I explore chivalry and knighthood as a specific social code and institution of power, both of which are complex constructs beneath its veneer of idealism and romanticism. More prominently however, I discuss the interplay between chivalry as a system of power and one’s humanity. I argue that the Blue Lions path of Three Houses compellingly demonstrates this dynamic through its characters and their interactions together, while also shining a light on the reality of individuals beholden to institutional power. Although contemporary narratives may tend to misconstrue the past for dramatic effect, I believe there is value in examining them because they may conversely reveal previously overlooked aspects of historical concepts due to the biases and values of the period.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.040 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 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".