The Proscription of Terrorist Organisations in Australia
Bibliographic record
Abstract
The proscription of organisations has long been a central feature of legal regimes aimed at the suppression of terrorism. Australia is no exception. Going back many decades, the Commonwealth government has sought to meet the threat of political violence through the proscription of related organisations. In the wake of the September 11 terrorist strikes against New York and Washington, renewed efforts were made for the proscription of organisations in many national jurisdictions (for example, the United Kingdom, United States and Canada) as well as at the international level (for example, through the United Nations and the European Union. In Australia, the Commonwealth looked directly to the justifications offered by the United Kingdom’s Lord Lloyd of Berwick and Paul Wilkinson just a few years before. In their major Inquiry into Legislation Against Terrorism, Lord Lloyd and Wilkinson presented three principal rationales to explain the role of proscription in the prevention of terrorism: ease of proof; providing a basis for the criminalisation of fundraising and other activities of terrorist groups; and as a clear symbol of ‘public revulsion and reassurance that severe measures [are] being taken’.
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.
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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".