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Record W3125996943 · doi:10.1002/cb.188

Mobilizing the <i>hijab</i>: Islamic identity negotiation in the context of a matchmaking website

2006· article· en· W3125996943 on OpenAlexaff
Detlev Zwick, Cristian Chelariu

Bibliographic record

VenueJournal of Consumer Behaviour · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicHalal products and consumer behavior
Canadian institutionsYork University
Fundersnot available
KeywordsNegotiationContext (archaeology)Meaning (existential)IslamNormativeSociologyConstruct (python library)Identity (music)Service (business)Social psychologyOrder (exchange)PsychologyAestheticsMarketingBusinessPolitical scienceLawSocial scienceHistory

Abstract

fetched live from OpenAlex

Abstract This article explores the intended use and meaning of the hijab as a personal branding tool for Muslim users of an online matchmaking service. We analyze the motivations of male and female Muslim consumers for mobilizing the symbolism of the hijab as they construct online identities. We ask whether including information about ‘willingness to wear the hijab’ is motivated primarily by a desire to comply to normative rules of conduct or by an instrumental attitude driven by a desire to effectively build and communicate a personal online brand. Our results indicate that the meaning of the hijab is not fixed and uncontested but is dependent on the historical and social context of insertion. In the context of an online matchmaking site, the motivation to mobilize the hijab is predominantly instrumental. In addition, women are more likely to use the hijab for personal branding than men, whose motivation to mobilize the veil's cultural and traditional symbolism prevails. In accordance with previous research, we find that a higher degree of education reduces the likelihood of men and women to use the hijab in order to conform to community norms. Copyright © 2006 John Wiley & Sons, Ltd.

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 score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.002
Scholarly communication0.0030.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.021
GPT teacher head0.313
Teacher spread0.292 · 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

Citations25
Published2006
Admission routes1
Has abstractyes

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