Bibliographic record
Abstract
Terrorist events are breaking news for the media whose ethical responsibility can be debatable. Tactics of terrorism vary from kidnapping, hostage-taking, hijackings, and others up to mass destruction, including the use of nuclear weapons. Media responses and coverage strategies of such tactics also vary, with some reluctant to provide terrorists with the “oxygen of publicity.” Some striking similarities have appeared recently between the build-up to the war on Iraq begun by U.S. President George W. Bush's administration in 2002, culminating with the start of war in 2003, and the 2012 push by current U.S. President Barack Obama for action to prevent Iran from acquiring a nuclear weapon. In the earlier case, the presumption was established in the public mind, without adequate evidence, that Iraq possessed or was about to possess weapons of mass destruction, and had the will to use them against the United States. In the latter case, the background presumption is that Iran is actively seeking to produce a nuclear weapon, with Israel as a potential target. This claim also lacks solid evidence at the time of writing, but has come to be accepted in some media as an uncontroversial fact. This chapter looks at aspects of how different English and French Canadian newspapers, as examples, covered the push for war on Iraq. It includes reflections on the use of language in reporting on the war itself. The central concern is with the media role in fear-mongering and propaganda for war.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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".