Bibliographic record
Abstract
The ease, speed and sophistication with which extremist groups have exploited cyberspace for operational coordination and ideological proselytising have taken Western governments by surprise. From brazen digital advocacy of extreme and violent ideology to deft recruitment and fundraising, the Internet has proven to be a remarkably useful medium for non-state actors and hostile terrain for states seeking to curtail the growing global influence of violent extremism. This chapter charts the trajectories of policy frameworks of one distinct cluster of states confronting similar challenges in this respect: the “Anglosphere” states of Australia, Canada, New Zealand, the United Kingdom and the United States of America. The apparent challenge for these Anglosphere states is that policy officials recognise that transnational counter-terrorism challenges cannot be resolved unilaterally but require collaboration in two crucial dimensions. First, to achieve meaningful sovereignty over cyberspaces requires government to acquire the cooperation of private sector actors – including large multi-national digital technology firms. For these companies, relinquishing commercial data or giving up encryption to authorities is anathema. Second, as extremist operational and proselytising activities can transfer across jurisdictions effectively instantly, states have sought to build multi-jurisdictional coalitions, pooling expertise, intelligence and, most importantly, resources. This chapter articulates how these imperatives have played out in domestic institutional settings and goes on to describe how Anglosphere states have forged robust though low-profile networks of security collaboration that facilitate policy and operational interchange. The Anglosphere transgovernmental alliance, it is contended, operates as a persistent and influential mode of policy-making for all partners, cognitively framing the “problem” of extremism in cyberspace and underpinning significant technical and strategic collaboration.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".