How Western Non-EU States Are Responding to Foreign Fighters: A Glance at the USA, Canada, Australia, and New Zealand’s Laws and Policies
Bibliographic record
Abstract
The issue of foreign fighter mobilisation to the Syrian conflict (and more recently Iraq) is the biggest security challenge for Western nations since the September 11th attacks. This is the first time since those events that governments all over the world including the West are beginning to rethink their legal regimes and reforms related to how they deal with this particular problem set. This chapter will look at ‘Five Eyes’ countries except for the United Kingdom (‘Five Eyes’ refers to the intelligence alliance amongst these countries). It will explore the United States’, Australia’s, Canada’s, and New Zealand’s responses to the unprecedented foreign fighter phenomenon over the past few years. These four case studies will provide a comparative perspective that will help show how they are changing in either similar or unique fashions. This will allow insights to be ascertained into a broad range of ways to deal with this issue on a legal level spanning different countries’ sizes and mobilization sizes. The organization of this chapter will include: first, an introduction that discusses the issue of foreign fighters and Syria and how that is affecting governments in these particular countries and the threats they perceive for if and when individuals return home. This will follow with case studies looking at each country’s particular responses from the United States to Australia to Canada to New Zealand. Finally, there will be a concluding section that provides a comparative look at these four different countries’ approaches.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.016 | 0.009 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".