A Framing Analysis of News Coverage of Iran’s Nuclear Deal with the United Nations \nSecurity Council’s Five Permanent Members (the P5+1) \nin the Islamic Republic News Agency and The New York Times
Bibliographic record
Abstract
Over the last several years, the issue of Iran’s development of nuclear power has caused significant stress among Western democracies. Israel, in particular, has perceived this as an imminent threat to its existence. Iran’s nuclear development has led to severe sanctions imposed by the United States and European countries that have severely crippled Iran’s economy. The effect of these sanctions prompted the Iranian government to start negotiations with the P5+1 to broker a deal that would see the economic sanctions removed in exchange for putting a stop to its nuclear development plan. Iran had cut political and economic relations with the United States since the 1979 Islamic revolution, so these nuclear deal negotiations were the first face-to face negotiations with the United States in over three decades. In order to discover how the nuclear deal was presented by the Islamic Republic News Agency (IRNA) and the New York Times to their readers, this thesis undertakes a textual and framing analysis of the news coverage during the month of July 2015. It concludes that despite the negotiations that reached a signed deal, the IRNA framed the United States as an “enemy” and the New York Times framed Iran as an “enemy.” This thesis also analyzed the political structure of both countries in relationship to journalistic norms practiced in each country, looking particularly at the notion of objectivity, or fairness and balance.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".