MétaCan
Menu
Back to cohort
Record W2159979300 · doi:10.1007/s13524-015-0376-3

From Parent to Child? Transmission of Educational Attainment Within Immigrant Families: Methodological Considerations

2015· article· en· W2159979300 on OpenAlexaff
Renee Luthra, Thomas Soehl

Bibliographic record

VenueDemography · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicIntergenerational and Educational Inequality Studies
Canadian institutionsMcGill University
FundersSpencer FoundationEconomic and Social Research CouncilSage FoundationRussell Sage Foundation
KeywordsImmigrationEducational attainmentHuman capitalDemographic economicsSocial mobilitySociologyDemographyDevelopmental psychologyGeographyPsychologyEconomicsEconomic growthSocial science

Abstract

fetched live from OpenAlex

One in five U.S. residents under the age of 18 has at least one foreign-born parent. Given the large proportion of immigrants with very low levels of schooling, the strength of the intergenerational transmission of education between immigrant parent and child has important repercussions for the future of social stratification in the United States. We find that the educational transmission process between parent and child is much weaker in immigrant families than in native families and, among immigrants, differs significantly across national origins. We demonstrate how this variation causes a substantial overestimation of the importance of parental education in immigrant families in studies that use aggregate data. We also show that the common practice of "controlling" for family human capital using parental years of schooling is problematic when comparing families from different origin countries and especially when comparing native and immigrant families. We link these findings to analytical and empirical distinctions between group- and individual-level processes in intergenerational transmission.

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)0.132
metaresearch head score (Gemma)0.224
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.132
Threshold uncertainty score0.700

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1320.224
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.005
Science and technology studies0.0040.004
Scholarly communication0.0030.003
Open science0.0040.005
Research integrity0.0020.002
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 head0.199
GPT teacher head0.420
Teacher spread0.221 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations51
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueDemographySame topicIntergenerational and Educational Inequality StudiesFrench-language works237,207