Bibliographic record
Abstract
Emerging from the “quality TV” shows of the early 1980s, contemporary American television shows such as The Sopranos (HBO, 1999 - 2007), Lost (ABC, 2004 - 2010), Game of Thrones (HBO, 2011 - ) and Breaking Bad (AMC, 2008 - 2013) have been frequently praised by critics and scholars for their narrative complexity. However, often neither critics nor scholars define what narrative complexity specifically constitutes. That is to say, what are intricate plotlines? What distinguishes complex characters from “simple” ones? And in what ways do complex television narratives differ from complex feature films? This study takes a cognition-based approach to the topic and discusses the AMC series Breaking Bad as one of the prime examples of narrative complexity in contemporary television. The series revolves around Walter White (Bryan Cranston), a fifty year old high-school chemistry teacher, who is diagnosed with inoperable lung cancer and decides to team up with a former student of his to produce methamphetamine in order to secure a financial future for his family before he dies. Breaking Bad frequently uses “puzzling” narrative devices such as flashbacks, flashforwards, time-jumps or cold opens and aligns its viewers with a main protagonist whose actions are often morally objectionable. During the course of this study, which is primarily based on the works of theorists such as David Bordwell, Edward Branigan, Thomas Elsaesser , Murray Smith and Jason Mittell, I discuss how narration in visual media storytelling operates, what narrative complexity in the television medium constitutes, and how watching “Complex TV” has changed how viewers process television narratives on a cognitive level. In particular, I explore the ways in which contemporary television narratives have adopted trademarks of what Elsaesser has termed “mind-game” films and how engaging with complex characters over the course of several seasons of a series can influence our understanding of the narrative as a whole. However, the study of “Complex TV” has only begun and this work is primarily supposed to generate more discussion about a narrative trend that has left its mark on the current “Golden Age of Television.”
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.025 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".