Bibliographic record
Abstract
Introduction The 9/11 terrorist attacks came as a shock to many. As discussed in the previous chapter, they resulted in an unprecedented amount of counter-terrorism activity by the United Nations (UN) Security Council, with little attention paid to human rights. A common narrative that emerged from 9/11 was that terrorism was taken more seriously when it came to the West. This narrative underestimated the degree of terrorism experienced in the West and, in particular, in the United Kingdom, where more than 3,000 people also died, albeit over a much longer period, in Northern Ireland. At the same time, however, the narrative was a powerful one because many democracies and the UN were prepared to enact much harsher laws in response to 9/11 than they had enacted in response to previous acts of terrorism. In his pre-9/11 comparative survey, David Charters has found that democracies were more likely to react harshly to prolonged domestic terrorism than international terrorism but that “democratic checks and balances worked” with the harshest states, Britain and Israel, limiting their response to geographically contained areas. As will be seen, the same cannot be said of the post-9/11 democratic experience as even democracies, such as Australia, that had experienced very little terrorism dramatically and quickly expanded their antiterrorism laws.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".