Terrorism and state repression of human rights: A cross-national time-series analysis
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
This study examines the major factors that predict states’ repressive policies, focusing on the relationship between oppositional terror attacks and state repression of core human rights. We rely on a theoretical framework that brings together actor-oriented explanations and socio-cultural approaches. While the former emphasize purposive rational action, international pressures, and domestic threats, the latter focus on the power of ideas and on processes of policy diffusion and cultural norms. Relying on a longitudinal cross-national analysis of panel data for the years 1981–2005, we find substantial evidence for the effects of both actor-oriented measurements and socio-cultural ones. These findings join a growing body of research that emphasizes the importance of the institutional and cultural determinants of states’ counterterrorist policies.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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