"A Comparative Analysis of the Framing of Terrorism in Online News Under the George W. Bush and Barack H. Obama Administrations: from Clash to Dialogue?"
Bibliographic record
Abstract
Abstract: The significant and reciprocal link between Terrorism and News Media reportage was identified in the 1990s by the convergence of security and media studies (Picard, 1993). Working in the tradition of content analysis early studies examined the way Terrorism was framed in major news accounts with implications for the Huntington-Fukuyama hypothesis (Nacos, 2002). However these early studies mainly pre-dated both the War on Terror and the rise of the Internet as a major (political) news source. This paper reinvigorates this early framing research on Terrorism by examining key frames over time in online news media via software-assisted media mapping. Key frames identified in the 1990s are examined in the online news environment under the George W. Bush administration (2005) and the current Barak Hussein Obama administration (2009). The resulting news mapping allows for a comparative analysis of the way frames, such as "insurgent" have changed (if at all) over time in the most significant stories freely available online. The comparison contributes to understanding of the potential for a shift from the rhetoric of a "clash of civilizations" towards discourse networks of the "dignity of difference" by rendering visible shifts in frames associated with Terrorism that arguably are necessary for the advancement of the new paradigm.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".