Addressing the Foreign Terrorist Fighter Phenomenon from a Human Rights Perspective
Bibliographic record
Abstract
Foreign terrorist fighters raise security concerns with regard to their actions abroad but also their possible return to their home countries. This paper asks whether tough state responses and new powers such as detention and withdrawal of citizenship raise significant human rights issues. It looks firstly at the different types of rights in play before examining counter-terror legislation from countries such as uk , Australia and Canada. Discourses of the profiles of extremists can be reductionist, ignoring the complexity of the journeys in and out of violent extremism. Does imprisonment does have a deterrent effect? What is the impact on communities of rendering individuals stateless? How does legislation impact on freedom of speech? The paper looks at good practices in deradicalisation from different countries, before outlining three key propositions. First is a much wider public education forum which explains different types of rights and encourages dialogue about what rights take precedence in a security strategy. Second is the forging of long term partnerships with communities, to build trust rather than stigmatise; and third is a greater democratisation of security policy, using two-way information and learning, from sources such as former extremists as well as from the voices of youth.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".