Special Data Feature Legislative response to international
Bibliographic record
Abstract
This article presents a new dataset dubbed LeRIT which identifies the legislative response to international terrorism in 20 liberal Western democracies, 2001–08. The dataset distinguishes 30 regulations governments may implement with the intention of reducing the risk of terrorist attacks. LeRIT covers legislation dealing with, inter alia, the rights of the executive to intercept, collect and store communications for anti-terrorist purposes, changes in pre-charge detention for terror suspects and modifications of immigration regimes. I aggregate these distinct regulations into three composite indices, distinguishing according to the main target of regulations, citizens, suspects, and immi-grants. This dataset contributes to the analysis of the consequences of international terrorism and provides a detailed account of the patterns in the legislative response to international terrorism from 2000 to 2008. I show that while all liberal Western democracies reinforced their counter-terrorist legislation, the scope of countries ’ regulatory response to terrorism differed largely. Some countries (i.e. the UK and the USA) implemented the full battery of regulatory responses while others (i.e. Scandinavian countries but also Canada and Switzerland) remained reluctant to cut deeply into the net of civil rights for citizens, suspects and immigrants alike. To further demonstrate the potential usefulness of the dataset, the article includes an example of analysis on the legislative response to international terror-ism. The reported baseline model suggests that a combination of risk assessment and political factors influence gov-ernments ’ willingness to cut deep into the net of civil rights.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.039 | 0.037 |
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".