Bibliographic record
Abstract
Until recently, internal use of the armed forces has been generally regarded by the public, as well as academic commentators, as conduct to be expected of a military or autocratic regime, not a democratic government. There is however growing concern that the 'war on terror' has been used to condition public opinion to accept the internal deployment of the armed forces, including for broader industrial and political purposes. This book examines the national and international law, human rights and civil liberties issues involved in governments calling out troops to deal with civil unrest or terrorism. As the introduction of military call-out legislation has become an emerging global trend in the opening years of the 21st century, there is considerable and growing interest in the constitutional and related problems surrounding the deployment of military forces for domestic purposes. Examining the changes underway in six comparable countries, the United States, Canada, Britain, Germany, Japan and Australia, this book provides a review and analysis of this trend, including its implications for legal and political rights.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 teacher head, 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".