"War on Terror" Via a "War of Words": Fear, Loathing, and Name-Calling In Hollywood's War's In Afghanistan and Iraq
Bibliographic record
Abstract
War on Terror" via a "War of Words" | 11 "war on terror" via a "war of words": Fear, loathing and name-calling in Hollywood's wars in Afghanistan and iraq Anjali Pandey 1 9-11 And tHE wAR on tERRoRThe causal connection between the September 11, 2001 attacks and the war in Afghanistan and Iraq was strategically and carefully construed on the part of the Bush Administration-providing a perfect political alibi for the subsequent U. S. military invasion and occupation of these two sovereign nations.Former President Bush is famously remembered for declaring: "We wage a war to save civilization itself.We did not seek it.But we will fight it" (Excerpts, 2001, p. 6).Across the Atlantic, Tony Blair's matching war cry was: "To safeguard peace, we have to fight" (Cowell, 2001, p. 6) Also chronicled is the Anglo-American alliance formed to combat the global "war on terror" (Hoge, 2001) with Britain vowing to stand "shoulder to shoulder with the US" (Ford, 2001, p. 7) defending itself against what President Bush called "acts of unimaginable horror" (Excerpts, 2001, p. 6), and his British ally called "the worst terrorist outrage" (Cowell, 2001, p. 6).A decade later, the wars still rage both on the ground and on the silver screens of Hollywood.The overt attacks on the people and places in these two war-zones have had a noticeable ripple affect domestically in terms of 'reported' public sentiments and reactions.Most are aware for example of the firestorm of fear-driving attempts in the recent, highly publicized church-sponsored Koran-burning event.Equally prominent is the inflammatory reaction from some New Yorkers to the building of a mosque near Ground zero.Adams (2010, p.5) reports on these events:
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.022 | 0.022 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".