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Record W2336643330 · doi:10.11575/prism/9485

Cultural Identity as Part of Youth’s Self-Concept in Multicultural Settings

2005· article· en· W2336643330 on OpenAlexaffvenueabout
Nazilla Khanlou

Bibliographic record

VenueLibraries and Cultural Resources (University of Calgary) · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicRacial and Ethnic Identity Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCultural identityMulticulturalismEthnic groupIdentity (music)Cultural group selectionSocial psychologyPsychologyCultural diversitySelf-conceptGender studiesSociologyAnthropologyPedagogy

Abstract

fetched live from OpenAlex

Identity is recognized as an important aspect of psychosocial well-being. This study examined the self-concept and cultural identity of 550 youth in a community based sample of high school students in Canada. A revised version of Kuhn and McPartland’s (1954) Twenty Statement Test and Oetting and Beauvais’ (1991) orthogonal cultural identification item were used to gather data. The relationship between participants’ individual (age and gender) and environmental (cultural background and migrant background) with cultural identity levels was considered. Close to 79% of respondents were born in Canada, 18% had immigrated, and 2.5% were visa students. The average age of respondents was 17 years. In relation to self-concept, 61.3% of responses were related to the Self-Evaluations category and 16.5% to the Social Identity category. Five sub-themes (Ethnicity/National origin, Migration status/Residency, Race, Language, and Cultural/Political) were related to cultural identity. Over 54% of the sample identified a lot and 32.5% identified some with the Canadian way of life. Cultural identity levels were found to vary by cultural background in relation to several cultural identity groups. The concept of neighbourhood concordance was considered among the explanations for emerging patterns. The term multiculturation was proposed in cultural identity discourse in multicultural settings.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.315
Threshold uncertainty score0.979

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.003
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.018
GPT teacher head0.257
Teacher spread0.239 · 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 teacher head, 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

Citations8
Published2005
Admission routes3
Has abstractyes

Explore more

Same venueLibraries and Cultural Resources (University of Calgary)Same topicRacial and Ethnic Identity ResearchFrench-language works237,207