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Record W2568396595 · doi:10.1163/18719732-12341343

Addressing the Foreign Terrorist Fighter Phenomenon from a Human Rights Perspective

2016· article· en· W2568396595 on OpenAlexaboutno aff
Zubeda Limbada, Lynn Davies

Bibliographic record

VenueInternational Community Law Review · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsnot available
Fundersnot available
KeywordsHuman rightsTerrorismLegislationFreedom of informationLawPolitical sciencePublic international lawNational securityImprisonmentForeign policySociologyPublic relationsInternational lawPolitics

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.962
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.161
GPT teacher head0.421
Teacher spread0.260 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2016
Admission routes1
Has abstractyes

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