Virtue at Hogwarts: Aristotle's Nicomachean Ethics Through The Lens of J.K. Rowling's Sorting System
Bibliographic record
Abstract
Exploring how Aristotle’s Nicomachean Ethics is reflected and developed through the Sorting System of J.K. Rowling’s magical school of Hogwarts in her Harry Potter series, this paper considers each house and its characteristic virtues individually. Alongside a comparison of a particular virtue as presented in Rowling’s work to Aristotle’s account, each chapter includes a dissection of the students who represent and problematize these accounts. This format allows an exploration of how the literary text converses with an ancient account of moral virtue. The first chapter considers Gryffindor House and its virtue of courage, the first virtue that Aristotle himself considers in the Nicomachean Ethics. The willingness of Gryffindor students to fight and die is placed alongside Peter Pettigrew’s apparent cowardliness in order to consider Aristotle’s various accounts of courage and apparent courage. Ravenclaw’s virtue of intelligence is considered second, and Helena Ravenclaw’s immoral actions are explored in the context of Aristotle’s work on lack of self-restraint. The third chapter sets out the discussion of Aristotle’s elitism next to Slytherin House’s requirement of not only ambition but pure-blood as well. The problematic characters of Horace Slughorn and Regulus Black, within this context, present a positive turn away from the flawed requirements of Slytherin and towards virtue found in complete friendships. The thesis’ final chapter focuses on Hufflepuff House, whose virtues, focussing on inclusion and hard work, are not easily matched with Aristotle. Rowling’s decision not to present a Hufflepuff character who reflects poorly on these virtues presents a more positive account of virtue available to everyone. The full thesis concludes with the recognition that, while Hufflepuff is presented as the most inclusive and well rounded of the houses, it too fails to bring all the virtues together. For the best and fullest moral lives, one must work hard to improve all of one’s virtues, no matter one’s social standing, material goods, or aims for the future. Individual virtue should be used to help improve society as a whole, and failure to see beyond one’s self, as the Harry Potter series shows, leads to destruction.
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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 | 0.001 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".