Climate Change: Eco-Dystopia in Antonio Scurati’s La seconda mezzanotte
Bibliographic record
Abstract
This article offers an examination of the novel La seconda mezzanotte (2011) by Antonio Scurati. Classified by the author as a catastrophist sci-fi novel, this work is here defined and analyzed as one of the very first Italian examples of climate fiction (cli-fi), a narrative form especially popular in North America, closely related to anthropogenic climate change. The essay discusses some of the topoi that characterize Anglo-Saxon cli-fi, which are clearly present in The Second Midnight. Such recurring motifs will also be discussed, in particular, by highlighting some of the rhetorical tools adopted by Scurati, such as, for example, the effect of estrangement. Through such effects, the reader is spurred to adopt an uncomfortable, “oblique” and unusual gaze. This study highlights Antonio Scurati’s skill and originality in dealing with the causes and global effects of climate change. The author succeeds in making the reader perceive the spatial and temporal magnitude of global warming, which in 2072 Venice materializes dramatically in a Big Wave. The Second Midnight is a human comedy that expands across centuries and continents while narrating the events of a few men living in Nova Venezia in a specific year, 2092. Distant spaces, remote times, human and non-human corporealities converge in a new Venice, which is a piece of a hologram narrating other people’s stories.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| 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".