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Record W3116181408

Culture, immigration and femininity perception: Comparing young Iranian, Canadian, and Iranian-Canadian immigrant women

2015· article· en· W3116181408 on OpenAlexaboutno aff
fatemeh Hamzavi-Abedi, Fatémeh Baghérian, Mohammad Ali Mazaheri

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicSocioeconomic Development in MENA
Canadian institutionsnot available
Fundersnot available
KeywordsImmigrationFemininityGender studiesPerceptionPolitical scienceSociologyPsychology
DOInot available

Abstract

fetched live from OpenAlex

This study compares young women’s perception of femininity in three sample of iranians, canadians and iranian-canadian immigrants to understand femininity in two different cultures, and also the immigrants’ position in comparison to source and destination countries. 45 iranian, 21 canadian, and 19 immigrant students participated in focus group discussions of femininity and its norms. Data was coded using content analysis method and frequency of each theme was counted. Frequency of common themes in these three groups was compared by chi square, and then themes were compared 2 by 2 using independent t-test. Content analysis of data revealed nine themes in iranian’s, six in canadian’s, and nine in iranian-canadian immigrants’ perception of femininity. There were five common themes, including “success orientation” and “feminine personal traits” and several distinguishing themes like “chastity” and “personal safety”. Group-specific norms are congruent with more general cultural differences in Iran and Canada, and also special situation of immigrants’ life.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.186
Threshold uncertainty score0.375

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.261
GPT teacher head0.510
Teacher spread0.248 · 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

Citations2
Published2015
Admission routes1
Has abstractyes

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