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Record W3212776912 · doi:10.32920/ryerson.14656245.v1

"Can I not wear my hijab in peace?" : understanding young Muslim girls reason for and experiences of wearing the hijab

2021· preprint· en· W3212776912 on OpenAlexaboutno aff
Farheen Khan

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicTerrorism, Counterterrorism, and Political Violence
Canadian institutionsnot available
Fundersnot available
KeywordsIslamSocializationFocus groupPsychologyIdentity (music)Gender studiesSociologySocial psychologyAestheticsArtGeography

Abstract

fetched live from OpenAlex

The aim of this research paper was to explore the reasons for and experiences of young Muslim girls wearing the hijab. Their decision to wear the hijab is examined by exploring the concept of choice within the framework of socialization. The participants included 4 young Muslim girls in the age range of 11-13 wearing the hijab and attending Canadian public school. Focus group and individual interviews were used for data collection. The results showed that religion was the primary reason why these girls chose to wear the hijab followed by their desire to develop a cultural identity and to represent Islam in the North American society. Family, peers and media were found to have an effect on their decision to wear the hijab. The girls narrated positive as well as negative experiences in and out of school, but were determined in their decision to wear the hijab and were happy with their decision. The implications and limitations of the study indicate a need for future research on this topic.

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)0.005
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 score0.012
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0050.006
Scholarly communication0.0050.003
Open science0.0010.003
Research integrity0.0020.003
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 head0.087
GPT teacher head0.341
Teacher spread0.254 · 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 routes1
Has abstractyes

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