The (Anti-)Hero with a Thousand Faces: Reconstructing Villainy in <i>The Sopranos</i>, <i>Breaking Bad</i>, and <i>Better Call Saul</i>
Bibliographic record
Abstract
The Sopranos, Breaking Bad, and Better Call Saul exemplify what Martin Shuster calls “new television.” Following 9/11, the playful irony that had defined shows such as Seinfeld, Buffy the Vampire Slayer, and The Simpsons was gradually replaced with more morally ambiguous characters. Questions about one’s moral standing operated at the forefront of such television, where previous idols of goodness on the right side of the law, such as Agent Dale Cooper ( Twin Peaks), Agents Mulder and Scully ( The X-Files), and Buffy Summers, gave way to characters whose moral dubiousness and criminal behaviour utterly destabilized long-standing characteristics of television, asking viewers to sympathize with imperfect, often unscrupulous individuals. In this article, I discuss the moral trajectories of a number of key characters, including Tony Soprano, Walter White, and Jimmy McGill. White’s and McGill’s alter egos (Heisenberg and Saul Goodman, respectively) represent two very different moral compromises in a world defined by white-collar corruption, self-sabotage, and a questionable judicial system that simultaneously protects criminals as it reprimands them. These two characters in particular exemplify a radical break with traditionally morally upstanding characters and have taken audiences into a new frontier of morally ambiguous television that celebrates the complex world of the anti-hero.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.012 | 0.012 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".