Bibliographic record
Abstract
In the 2004 US presidential debates, Democratic nominee John Kerry strongly criticised the manner in which the Bush administration had launched the war in Iraq in 2003. His foremost criticism was that the campaign was not sufficiently multilateral, leaving the USA to bear ‘90 percent of the casualties in Iraq and 90 percent of the costs’. He suggested that closer attention to alliance-building and more engagement with the United Nations would remedy the situation. This line of attack was well calculated to appeal to US public opinion. George W. Bush responded that he had in fact engaged the UN and that Kerry was undervaluing the coalition that had been built for the Iraq campaign. However, he was clearly on the defensive and struggled to fend off Kerry's repeated charge that ‘we can do better’ at coalition-building. Kerry's second line of criticism was decidedly less successful. He charged that the Iraq campaign failed ‘the global test where your countrymen, your people understand fully why you're doing what you're doing and you can prove to the world that you're doing it for legitimate reasons’. Kerry suggested that the Bush administration's refusal ‘to deal at length with the United Nations’ was part of the reason for this failure. This line of attack backfired. Bush immediately challenged the notion of a ‘global test’ as undermining the USA's right to protect itself and referred to it in subsequent debates to portray Kerry as insufficiently committed to US security.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.340 | 0.195 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".