Bibliographic record
Abstract
By drawing upon Siegfried Kracauer’s concept of cinema as a “mirror” of society, this article explores the impact of the “terror years” since 2001 on US cinema. Hollywood was the main cultural apparatus for coping with 9/11, which had left Americans struggling in the “desert of the real” (Žižek). Visual content simplifies traumatic events like the terrorist attacks for audiences—often expressing them in simple Manichean black and white terms and thereby offering moral guidance, unity, and a sense of destiny. Hollywood’s response to 9/11 included all these different aspects: It appealed to an “unbroken” spirit, strove to reassert the symbolic coordinates of the prevailing American reality, and mobilised for a response to new challenges. With time passing, Hollywood also incorporated the mounting doubts and dissent associated with this process. As the review of relating movies of the “terror years” demonstrates, the American film industry has examined, processed, and interpreted the meaning of the terrorist attacks in great variety: Ranging from merely atmospheric references to re-enactments, from pro-war propaganda to critical self-inquiry.
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 | 0.003 | 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 | 0.006 | 0.012 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".