K-drama in the time of the pandemic: the production of hope through the subgenre of the K-Dystopia
Bibliographic record
Abstract
During the last quarter of 2021, as the world was preparing to exit the \nCOVID-19 pandemic, Netflix released three original dystopian K-drama \nseries: Squid Game (September), Hellbound (November), and The Silent \nSea (December). Through textual analysis of these three series, this \npaper delves into the ways in which the pandemic served to create a \nsense of hopelessness and helplessness, a social mood which served as a \nbackdrop for the K-dystopia subgenre to thrive. Paradoxically, perhaps, \nthese dystopian narratives found creative ways to re-package and re- \npresent the concept of hope. In this way, the new genre both built on \nand veered away from the previous success of K-dramas that presented \nhope through the element of romance. The dystopian trend might also \nbe considered a Netflix counterprogramming strategy. Finally, the paper \nexplores how realism is incorporated into these dystopian storylines, \nreflecting the gloomy side of Korean society. Here, realism involves the \ntheme of social injustice, which may be contrasted with the brighter side \nof Korean culture—typically communicated through the K-pop craze \nthat offers (and sells) fun, glitz, and glamour.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".