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Record W3023159814 · doi:10.1080/13676261.2020.1757632

‘You can’t just be a Muslim in outer space’: young people making sense of religion at local places in the city

2020· article· en· W3023159814 on OpenAlexaboutno aff
Laura Kapinga, Bettina van Hoven

Bibliographic record

VenueJournal of Youth Studies · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicReligious Tourism and Spaces
Canadian institutionsnot available
Fundersnot available
KeywordsSalience (neuroscience)NegotiationSociologyGender studiesContext (archaeology)NarrativeSense of placeMeaning (existential)Sociology of religionMeaning-makingLived religionAnthropologyPsychologyGeographySocial sciencePolitical scienceArt

Abstract

fetched live from OpenAlex

This paper demonstrates how young people make sense of religion through local places in the urban context while moving from youth to young adulthood. We draw on in-depth interviews – including a mental map-making exercise – with twenty-four young Muslims (18–30) from a wide range of cultural backgrounds living in Metro Vancouver (Canada). Their narratives reveal young people ‘live’ religion in various local places and how spatialities of lived religion change over time. We highlight how making sense of religion is reflected in the changing meaning of the mosque and relates to the increased salience of places shared with young Muslims in which our participants negotiate religion in the context of their everyday lives in the city. While many studies on Muslim identities have established the complexities and dynamics of negotiating religion at specific local places, we argue for a focus on relations between lived religion at various local places over time. These spatiotemporal complexities are able to capture how making sense of religion is spatially and fluidly manifested in the urban context of Metro Vancouver.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.311
Threshold uncertainty score0.619

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.006
Scholarly communication0.0050.002
Open science0.0010.005
Research integrity0.0010.003
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 head0.067
GPT teacher head0.333
Teacher spread0.265 · 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

Citations11
Published2020
Admission routes1
Has abstractyes

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