Climate Change Vulnerabilities: Eco-Anxiety and Ethics of Posterity in Alan Gratz’s Two Degrees
Bibliographic record
Abstract
Climate change presents vulnerabilities, epitomized by extreme weather phenomena and enduring environmental transformations that imperil welfare of human existence in the Anthropocene. In his novel Two Degrees (2022), Alan Gratz engages with this existential issue following the trajectories of three youthful protagonists: Akira, Owen, and Natalie. In the backdrop of wildfires in California, polar bear encounters in the Canadian tundra, and a cataclysmic hurricane in Miami, the novel adeptly illustrates the imminent and perilous threats of eco-disasters caused by a rapidly changing environment. The interwoven narratives of Akira, Owen, and Natalie subsequently reveal the imperative for collective action and strategic foresight to avert escalating climate catastrophes and protect succeeding generations from the plight of climate-induced displacement. This contextualizes Two Degrees within the ethical purview of posterity and accentuates the moral obligation to shield future generations from catastrophes. This study examines the portrayal of climate change and its psychological repercussions, specifically focusing on the eco-anxiety experienced by the protagonists. Additionally, it explores how these characters embody the ethical responsibility incumbent upon contemporary generations to confront climate change and safeguard the welfare of posterity. Using the theory of econarratology of Arran Stibbe, this study presents Two Degrees not merely as a riveting narrative but as a clarion call to action, beckoning readers to engage in environmental advocacy and contribute substantively to ameliorating climate change.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.021 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| 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".