Exploring the Relationship between Dystopian Literature and the Activism of Generation Z Young Adults
Bibliographic record
Abstract
Some recent research has posited that the independent and revolutionary traits of Generation Z can be traced to the circumstances of their births, specifically the 9/11 attacks and the Great Recession. While there has been research examining the effect of these events on the type of behavior Generation Z exhibits towards political and societal issues, there has been little research that examines the literary culture in which they grew up. Did popular dystopian works such as Catching Fire by Suzanne Collins (2009), Divergent by Veronica Roth (2011), and The Maze Runner by James Dashner (2009) have an impact on their political identities and behaviors? This paper examines that question by using a mixed method approach: a public questionnaire, thirteen in-depth interviews with a select group of Generation Z students from the University of Georgia, and direct content analyses of the key works under consideration. This study argues that the relationship between dystopian literature and young adult activism may offer insight into the ways literature can be used as a revolutionary tool. This study also hopes to add to the literature exploring the characteristics of Generation Z and the significance dystopian literature may have not only on a young adult’s thoughts but also their actions. KEYWORDS: Dystopian Literature; Dystopian Literary Media; Generation Z; Youth Activism; Literary Influence; Activist Typology; Aspects of Literary Response: A New Questionnaire; College Students; Divergent; Catching Fire; The Maze Runner; Literary Culture, The Hunger Games
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".