“Local Yearnings”: Re-Placing Nostalgia in Don DeLillo’s _Underworld_
Bibliographic record
Abstract
Many scholars have read DeLillo’s fiction as an illustration of “how conflicting postmodern practices collide” (Parrish 697). In this essay, I proceed from the recognition that the nature-culture binary is more fluid than ever and situate DeLillo as a theorist not only of postmodern culture but also of postmodern nature. I examine DeLillo’s most ambitious and complex novel, Underworld, through the lens of green cultural studies—a phrase I prefer to “ecocriticism” because it resonates with cultural studies’ interdisciplinarity, ideology critique, and attention to power. Combining a cultural studies methodology with more traditional ecocritical strategies, green cultural studies confronts networks of power while exploring the socio-environmental dimensions of a given text. A green cultural studies approach is particularly well-suited to addressing the novel’s compelling, often confounding, refractions of the postnatural condition. Using this critical framework, I suggest that this simultaneously nostalgic and ironic text, characterized by an alternately worshipful and irreverent treatment of nature, both maps and engenders a radicalized postmodern nostalgia—nostalgia with a critical edge. Unlike critics (such as Renato Rosaldo, William Cronon and others) who have theorized nostalgia’s limitations, Underworld shows how nostalgia can be harnessed and utilized in the service of social and environmental critique.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.023 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".