The Proportionality Principle, Counter-Terrorism Laws and Human Rights: A German-Australian Comparison
Bibliographic record
Abstract
As a general principle of law, some form of proportionality is found in most legal systems. It is, for example, readily applied in the context of criminal law where the severity of punishment is expected to be proportionate to the seriousness of the crime. The proportionality principle, moreover, is regarded as a fundamental element of regulatory policy and public administration. In this context, the principle is considered to find its origins in German constitutional and administrative jurisprudence. Over the past fifty years, however, it has become a preferred procedure for managing disputes involving an alleged conflict between two rights claims, or between a rights provision and a legitimate state or public interest. From its German origins, the proportionality analysis spread across Europe and into Commonwealth systems such as England, Canada, New Zealand, and South Africa. In Australia it still awaits formal recognition in constitutional law and administrative law. This article examines the application of the proportionality principle in the context of anti-terrorism law with particular reference to counter-terrorism measures in Germany and Australia. It analyses how - in the German context - the principle has played an important role in preventing undue invasions of basic rights for the purposes of countering terrorism. At the same time, the article seeks to demonstrate that the lack of formal recognition of the principle in Australia has lead to the adoption of a range of anti-terrorism laws that curtail civil liberties to an unprecedented extent.
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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 | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".