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Record W4232512928 · doi:10.22215/rera.v4i2.199

The Growing Influence of the Courts over the Fate of Refugees

2008· article· en· W4232512928 on OpenAlexvenueaboutno aff
Dagmar Soennecken

Bibliographic record

VenueReview of European and Russian Affairs · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsnot available
Fundersnot available
KeywordsRefugeeGermanInstitutionPolitical scienceRefugee lawIdentity (music)Subject (documents)LawSociologyGeography

Abstract

fetched live from OpenAlex

A number of migration scholars suggest that domestic courts have become the key protective institution for refugees. How can we explain this claim? One prominent explanation identifies group litigation as the key source of the increasing influence of the courts. How well does this explanation travel empirically? The article evaluates this explanation by examining the puzzling behaviour of German refugee NGOs. They have not entered the legal arena directly (either as parties or as interveners), nor have they concentrated on developing extensive litigation campaigns. Still, they are remarkably ‘judicialized’: their frequent engagement with the law in other respects has heightened their legal consciousness. Why have German refugee NGOs made such different choices than their North American counterparts and what do these choices tell us about the expanding influence of the courts over the fate of refugees in Germany and North America? To make sense of the different choices that these organizations have made, we need to understand the role that institutional norms and procedures, in particular policy legacies, have played in directing the behaviour and identity of these groups. For a number of reasons, German refugee NGOs historically have been discouraged from directly accessing the courts in favour of indirect participation. Since Canadian and American refugee organizations follow a pattern closer to the expectations of the (largely North American) literature on the subject, we need to be more careful in thinking through ou

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.955
Threshold uncertainty score0.380

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.011
GPT teacher head0.267
Teacher spread0.257 · 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.

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

Citations15
Published2008
Admission routes2
Has abstractyes

Explore more

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