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Record W1916974938 · doi:10.29173/cjs1651

Ethnic Differences in Educational Attainment among the Children of Canadian Immigrants

2008· article· en· W1916974938 on OpenAlexaffvenueabout
Teresa Abada, Feng Hou, Bali Ram

Bibliographic record

VenueThe Canadian Journal of Sociology · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicRacial and Ethnic Identity Research
Canadian institutionsStatistics CanadaWestern University
Fundersnot available
KeywordsEthnic groupImmigrationResidenceEducational attainmentGermanDemographic economicsSociologyPortugueseDiversity (politics)Social mobilityGender studiesSettlement (finance)DemographyPolitical scienceGeographySocial scienceAnthropologyEconomics

Abstract

fetched live from OpenAlex

Abstract Using the 2002 Ethnic Diversity Survey, this article examines the ethnic differences in university education attainment among the children of immigrants in Canada. We found that most groups achieve clear upward mobility across generations, while Blacks and Filipinos show signs of stagnation. Asians (with the exception of Filipinos) attain higher academic achievements than most groups of European origins even when accounting for group variations in family background, and social and ethnic capital. Parental education was important in explaining the relatively low university completion rates among the second generation Portuguese and Italians. Rural residence of the father’s generation was an important factor for the second generation Dutch and German youth, reflecting the different settlement patterns of these various groups. Our findings suggest that race/ethnicity has become a salient factor in educational stratification. Keywords: ethnicity, mobility, education, second generation, immigrants

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.043
Threshold uncertainty score0.087

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.090
GPT teacher head0.337
Teacher spread0.247 · 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 designObservational
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

Citations129
Published2008
Admission routes3
Has abstractyes

Explore more

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