Terror-Time in Network-Centric Battlespace: DeLillo’s Later Fiction
Bibliographic record
Abstract
Variously addressing the war on terror now waged in the “full-spectrum dominance” of network-centric battlespace, DeLillo’s most recent four novels (published immediately before and since the 9/11 attacks) work to produce stillness as opposed to seductive, terroristic speed—to detach identities from everyday practices of time and elaborate transformative experiences for the reader. Each novel constitutes a complex hetero-chronograph, a textual apparatus that not only measures time but also produces altered temporal forms that slow down and multiply felt times and durations. Redeploying Bakhtin’s notion of the text as an unrepeatable event co-produced by the author and reader, this article explores how DeLillo’s texts enable the reader to become an actor, to produce and experience modes of resistance to the totalizing forces of terror by the realization of a network of virtual narrative associations among economic, political, aesthetic, and ethical vectors. Such relations are mapped on a Greimassian semiotic square of “terror-time.”
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".