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Record W7054902644

The asylum-jihadism nexus: non-government actors and detection of jihadism among asylum seekers.: A case study of the Dutch Refugee Council and the guardianship organization for unaccompanied minors (Nidos).

2017· report· en· W7054902644 on OpenAlexaff

Bibliographic record

VenueData Archiving and Networked Services (DANS) · 2017
Typereport
Languageen
FieldEngineering
TopicLaser Design and Applications
Canadian institutionsL'Alliance Boviteq
Fundersnot available
KeywordsJihadismLegal guardianRefugeeSyrian refugeesGovernment (linguistics)Balance (ability)Duty
DOInot available

Abstract

fetched live from OpenAlex

This article explores why, how and to what extent two non-government organizations working with asylum seekers in the Netherlands engage in the detection of jihadism. It concludes that the organizations acknowledge the importance of sharing possible signs of jihadism with relevant governmental actors, but struggle in striking a balance between serving the interests of their clients and pupils, on the one hand, and assisting in identifying possible national security threats, on the other hand. The authors recommend a systematic study on the extent to which non-government actors (are willing to) engage in identifying jihadism amongst asylum seekers.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0120.007
Scholarly communication0.0050.003
Open science0.0010.006
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.026
GPT teacher head0.238
Teacher spread0.211 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations0
Published2017
Admission routes1
Has abstractyes

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