The Response of Cultural Studies to 9/11 Skepticism in American Popular Culture
Bibliographic record
Abstract
Thi s arti cle examines the response to 9/11 skepticism b y scholars in the field of cultural studies. A survey of recentbooks on 9/11 in Ame rican popular culture shows littl e consideration of 9/11 conspiracy theories in popular culture, and no consideration of legitimate forms o f skepticism . In addition, cultural studies criti cs such as Claire Birchall, Jack Bratich, Mark Fenster, and Jodi Dean have theorized the discourse of 9/11 conspiracy theories with an emphasis on how the conspiracies are arti culated butnotwhether t here are legitimate forms o f skepticism . To address this absence in the scholarship, this arti cle considers some of the omissions and distortions of the9/11 Commission Report. Itconcludes by citing recentarti cles in mainstr eam academic journals that str ongly indict t he official narrative of 9/11, and suggests the potential value of 9/11 skepticism to an anarchist cultural studies. * Michael Truscello is an assistantprofessor in English andGeneral Education atMount Royal University i Calgary,Albert a. His publications have appeared in journals such asPostmodernCulture, CulturalCritique, Affinities, Tech nicalCommunicationQu arterly and TEXT Tech nology. He discusses the nexus of technology and post-anarchism in Post-Anarchi sm: A Reader (2011) from P luto Press, YouTube and the anarchist tr adition in Transgression 2.0 (fort hcoming) from Con tinuum P ress, and humor and 9/11 skepticism in A Decade of Dark Humor: How Comedy, Irony, and Satire Shaped Post-9/11 Politics (2011) from University Press of Mississippi.
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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 | 0.019 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 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".