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Record W4248037205 · doi:10.32920/ryerson.14660457

Identity, fashion and dress consumption by immigrants : a focus on ethnic dress and/or hijab

2021· preprint· en· W4248037205 on OpenAlexaffabout
Omono Gladys Akhigbe

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicMiddle East Politics and Society
Canadian institutionsToronto Metropolitan UniversityUniversity of Toronto
Fundersnot available
KeywordsEthnic groupMulticulturalismDiasporaIdentity (music)Gender studiesImmigrationClothingConsumption (sociology)Religious identitySociologyCultural identityPolitical scienceAestheticsAnthropologyArtSocial scienceLaw

Abstract

fetched live from OpenAlex

This study highlights and explores how Canada’s multicultural policy influences the relationship between fashion and identity of racialized diaspora communities in Canada. It focuses on traditional dress and/or the Hijab, a religious dress among diaspora communities in Canada. However, the study demonstrates that consumption of these items among immigrants varies, with some immigrant communities displaying stronger ethnic identity than others. The consumption of these goods shapes one’s ethnic or religious identity. The two theoretical approaches shed insights on the complex relationship between ethnic fashion/dress, religious dress and ethnic identity. The study concludes that although symbols of ethnic identity such as ethnic fashion and/or religious dress are increasingly being contested due to political ideology, they have served members of their respective diasporic communities quite well in that they have allowed them to display and celebrate their identity, and thus produce a particular theme of their identity within Canadian multiculturalism. Key words: Fashion, ethnic dress/clothing, veil/religious dress, immigrants and diaspora.

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)0.001
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.830
Threshold uncertainty score0.341

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.002
Scholarly communication0.0030.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.080
GPT teacher head0.374
Teacher spread0.294 · 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
Published2021
Admission routes2
Has abstractyes

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Same topicMiddle East Politics and SocietyFrench-language works237,207