Bibliographic record
Abstract
This article argues that the variation in the use of torture as a mechanism of state terrorism can be best explained by recent changes in the global economy, the increasing influence of liberal-democratic political ideology, and the advent of anti-state Islamic terrorism. Specifically, although the use of state torture as a matter of policy is widespread, as societies shift from an agrarian society to an industrial and an advanced capitalist society, the disutility of policies of state torture increases primarily due to economic interdependence, the distribution of wealth, minimum standards of living, and the influence of the global media and the international community. While advanced liberal, capitalist states have employed state torture in the past and to a lesser extent more recently, these instances typically involve the use of torture against the citizens of other countries, privatizing torture organizations, or employing surrogate countries. The main focus of this article, however, is to explain the current growing disutility of policies of state torture as a form of state terror against real or perceived internal threats to the government or the state. Nonetheless, national variations in the extent or type of state torture will remain; however, this variation is primarily dependent on the type of macroeconomic structure of a country and its consequent economic integration in the emergent global economy.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".