Bibliographic record
Abstract
Abstract: How have authors responded to 9/11? This article examines a variety of reactions in light of the need to construct a dominant narrative necessary to restore a sense of security and understanding. But no single work or group of works has so far managed to trump the tragedy, and a void remains. The phrase “white rain” expresses the blizzard of paper generated by authors attempting to make sense of the event. Authors as diverse as John Updike, Don DeLillo, Frédéric Beigbeder, Jonathan Safran Foer, and Thomas Pynchon are the focus of the analysis here, showing how newly opened textual spaces are filled with works alternating between history and fiction. Of particular importance is the role of film, the importance of fear, and the symbol of flags in evaluating the process of recovery. Of particular note is the unease that characterizes the reaction of writers to the challenge they face: how to unite 9/11 as it exists in our cultural imagination with the event itself. That is the new task of literature in the post-9/11 age which, ironically, seems to require conflict or catastrophe for its inspiration.
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.024 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".