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Record W2035399514 · doi:10.1080/1360200042000212223

Islamic identity formation among young Muslims: the case of Denmark, Sweden and the United States

2004· article· en· W2035399514 on OpenAlexaboutno aff
Garbi Schmidt

Bibliographic record

VenueJournal of Muslim Minority Affairs · 2004
Typearticle
Languageen
FieldSocial Sciences
TopicJewish Identity and Society
Canadian institutionsnot available
Fundersnot available
KeywordsTransnationalismIslamIdentity (music)DiasporaContext (archaeology)MythologyState (computer science)Gender studiesSociologyIdentity formationLegislationPolitical scienceNational identityPolitical economyLawPoliticsGeographyNegotiationHistory

Abstract

fetched live from OpenAlex

This paper aims to explore aspects of transnational identity formation among young Muslims in three Western countries, Denmark, Sweden and the United States. The thesis is that, on the one hand, such transnational identity formations are indeed taking place, and, on the other, they are continuously effected by aspects of the local and the contextual, and in particular by the conditions and legislation of the host nation‐state. The process of transnational identity formation is described according to four overall conditions and themes: (1) visibility and aesthetics; (2) choice; (3) transnationalism; and (4) social ethics. These themes play significant roles on an overall transnational level, but are continuously ‘localized’, formulated and lived according to the context in which Muslims actually live. In the concluding section, the article discusses the implications of the dynamic field of transnational/national Muslim identity formations for the definition of a Muslim diaspora, and raises the question of whether we can at all talk about religious diasporas, and how we may do so on the basis of myth and politicized identities.

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)0.003
metaresearch head score (Gemma)0.002
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 score0.061
Threshold uncertainty score0.121

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0140.006
Scholarly communication0.0050.002
Open science0.0010.007
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.014
GPT teacher head0.272
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 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

Citations98
Published2004
Admission routes1
Has abstractyes

Explore more

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