Nature vs. Nurture in Albuquerque: What <em>Breaking Bad</em> and <em>Better Call Saul</em> Teach Us about How We Talk about Criminals
Bibliographic record
Abstract
Breaking Bad and Better Call Saul focus on the criminal transformation of their two main characters, Walter White (Bryan Cranston) and Jimmy McGill (Bob Odenkirk). While quite similar on the surface, Walter and Jimmy’s narratives represent two different criminal transitions, evoking the classic nature vs nurture conversation. Both of these shows bring the conversation to the idea of inevitability. The nature vs. nurture argument is a popular one because it acts as a teaching tool for how we think and talk about criminal behavior. At first, it follows that since criminality was in Walter White’s nature the whole time, his transition should feel the most inevitable, with the inverse being true of Jimmy. However, since Better Call Saul is a prequel to Breaking Bad, the opposite ends up happening. Even though Jimmy may only need the right people around him to be saved from his descent, his presence as Saul Goodman on Breaking Bad reminds the audience that it is Jimmy who is already fated to become a criminal. This dichotomy highlights the distinctive pedagogical opportunity present in both of these shows. Through their subversion of the concepts of nature and nurture, they allow for a unique teaching opportunity regarding how we talk about criminals. This article explores what they teach us and how their commentary can be used as a pedagogical tool for learning about criminal behavior in more nuanced ways.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.020 | 0.013 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".