Baba Yaga, Monsters of the Week, and Pop Culture’s Formation of Wonder and Families through Monstrosity
Bibliographic record
Abstract
This paper considers transforming forms and their purposes in the popular culture trope of the televised Monster of the Week (MOTW). In the rare televised appearances outside of Slavic nations, Baba Yaga tends to show up in MOTW episodes. While some MOTW are contemporary inventions, many, like Baba Yaga, are mythological and fantastic creatures from folk narratives. Employing the concept of the folkloresque, we explore how contemporary audiovisual tropes gain integrity and traction by indexing traditional knowledge and belief systems. In the process, we examine key affordances of these forms involving the possibilities of wonder and the portability of tradition. Using digital humanities methods, we built a “monster typology” by scraping lists of folk creatures, mythological beasts, and other supernatural beings from online information sources, and we used topic modeling to investigate central concerns of MOTW series. Our findings indicate connections in these shows between crime, violence, family, and loss. The trope formulates wonder and families through folk narrative and monster forms and functions. We recognize Baba Yaga’s role as villain in these episodes and acknowledge that these series also shift between episodic and serial narrative arcs involving close relationships between characters and among viewers and fans.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".