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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".