Bibliographic record
Abstract
Don DeLillo has shown considerable interest in terror, frequently depicting extreme dread of something terrible to happen, in his literary texts. Since more than three thousand innocent people in New York were killed by the 9-11 terrorist attack in 2001, the anticipation about what kind of fiction he would write as a New Yorker was high. DeLillo`s novel Falling Man (2007) in fragmentary detail represents the scene of the terrorism from the perspective of Keith Neudecker, a lawyer who escapes the collapsing world trader center. Neudecker`s post-traumatic stress disorder in the first chapter is followed by the free-associative portrayal of various impacts of the 9-11 terror on Neudecker`s wife Lienne in the second chapter. The random mixture of the first person narratives from such diverse view-point characters as Neudecker`s son Justin, relatives and friends, with dialogues and recollections yields a very close picture of the consequences of terrorism. Reading DeLillo`s Falling Man in juxtaposition with a Japanese Canadian novel Obasan by Joy Kogawa, reminiscences of the maltreatment of Japanese Canadians during and after the second world war, surfaces the authorial intention of the two novels. They as trauma literature emerge to aim at curing the readers and proposing post-traumatic ethics. Laurie Vickroy`s theory of trauma narrative and cure, E. Ann Kaplan`s theory of trauma witness narrative and responsibility, and Emmanuel Levinas`s theory of trauma memory and ethics offer theoretical grounds for the convincing analysis of the two texts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.015 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".