Bibliographic record
Abstract
One frequent technique for studying television is through genre. However, with the complex television environment of the 21st century, many genre studies do not adequately account for how generic television programs intersect other generic traits. This study probes how genre works within complex television narratives and proposes a new way of thinking about genre. Through Deleuze and Guattari's (1987) theory of the rhizome, I will suggest an interconnected understanding of genre characteristics. The television landscape is a complex, dynamic structure; the assortment of programs and the traits of those programs differ greatly from one moment to the next. Therefore, this study will propose a meta-theory that enables studying this landscape. The theory of the generic rhizome challenges simplistic readings of television texts; opens texts up to endless possible interpretations and insights; and it flattens cultural hierarchies. In two studies, which look at sitcoms and Westerns, I tease out this theory and study television shows in a way that mines, rather than flattens, the complexity of the medium.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".