Right-wing extremist threats to Australia in the context of the Christchurch terrorist attack
Bibliographic record
Abstract
This thesis seeks to examine whether the Christchurch terror attack in New Zealand in 2019 constituted a failure in Australia’s counter-terrorism strategy. A comparative case study analysis was undertaken of the Five Eyes (FVEY) countries’ counter-terrorism strategies to evaluate their effectiveness in mitigating RWE terrorism between 2014 and 2020. The findings revealed that Australia and New Zealand were slower to take action against the RWE threat, in contrast to the UK and Canada, and to a lesser extent, the US. Australia’s slowness was, in part, due to its complacency, given that Australia had not experienced any significant RWE attacks, unlike the US, UK and Canada. Another reason for Australia’s slowness was because of its disproportionate focus on Islamist extremist terrorism, driven by the predominantly anti-Muslim terrorism discourse of some mainstream politicians. The thesis also explores what Australia could do to counter the growing RWE threat.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".