Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
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 immigrants. 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 terrorism. The reported baseline model suggests that a combination of risk assessment and political factors influence governments' willingness to cut deep into the net of civil rights.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it