Post-September 11 Discourse: The Case of Iran in The New York Times
Bibliographic record
Abstract
This study examined how discursive strategies and related linguistic devices were employed by The New York Times (TNYT) to portray Iran after the terrorist attacks in the U.S. on September 11, 2001, and how the media representation may have contributed to negative and/or positive outcomes in terms of geopolitical relations. The study also investigated how sociopolitical assumptions were manifest in producing news about Iran and how the news discourse continued to shape the power relations between the nation and the U.S. in particular, and the world at large. Using Critical Discourse Analysis (CDA) as a multidisciplinary approach, the analysis focused on 171 front-page TNYT news articles from 2001 until 2009. Analysis of the discursive strategies and linguistic means revealed that the news media depicted an overall negative picture of Iran after the September 11 or “9/11” attacks. The effect of this rather stereotypical construction of Iran in TNYT was that of the negative Other, a nation of people that formed part of George W. Bush’s contentious “axis of evil” thesis–malevolent, untrustworthy, violent, and a threat to world peace.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.016 | 0.014 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| 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".