The Shock and Awe of the Real: Political Performance and the War on Terror
Bibliographic record
Abstract
The War on Terror has transformed the global political and cultural landscape of the last 20 years. How has it been represented in performance? The Shock and Awe of the Real is a transnational study of representation of the War on Terror in theatre, live art, protests, and new media. Looking at work by artists primarily from Arab and Middle Eastern diasporas working in the US, UK, Canada, and Europe, the study examines how modes of performance in live art, documentary theatre, and participatory performance respond to and comment on the power imbalances, racial formations, and political injustices of these conflicts. Many of these performances are characterized by a deliberate blurring of the distinctions between performance and reality. This has meant that playwrights crafted scripts from the real words of soldiers instead of writing plays; performance artists harmed their real bodies, replicating the violence of war; actors performed in public space; and media artists used new technology to connect audiences to real warzones. This embrace of the real contrasts with postmodern suspicion of hyper-reality—which characterized much political performance in the 1990s—and marks a shift in understandings of the relationship between performance and the real. These strategies allowed artists to contend with the way that war today is also a multimedia attack on the way that reality is constructed and perceived. The dissertation traces the historical antecedents of these aesthetics in postmodern criticism of prior generations of political performance and shows how these artists struggled to discover new aesthetic strategies to criticize war, anti-Islamic racism, and violence.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.039 |
| Scholarly communication | 0.017 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".