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Record W2595516991 · doi:10.3138/jcfs.46.4.541

Have You Set Your Life Priorities Straight?: Intergenerational Differences in Life Goals among European and East Asian Americans College Students and their Mothers

2015· article· en· W2595516991 on OpenAlexvenueno aff
Esther S. Chang, Chuansheng Chen, Elizabeth Kim

Bibliographic record

VenueJournal of Comparative Family Studies · 2015
Typearticle
Languageen
FieldPsychology
TopicPsychological Well-being and Life Satisfaction
Canadian institutionsnot available
Fundersnot available
KeywordsSimilarity (geometry)PsychologyEthnic groupLife satisfactionQuality of life (healthcare)Depressive symptomsSet (abstract data type)Family lifeDevelopmental psychologyAssociation (psychology)DemographyGerontologySocial psychologyGender studiesSociologyMedicine

Abstract

fetched live from OpenAlex

We examined ethnic differences in (a) the life goals of college-enrolled youth as well as their mothers’ goals for them, (b) intergenerational similarity in life goals, and (c) the association between intergenerational similarity in life goal priorities and parent-adolescent relationships and psychological adjustment. East Asian American youth and their mothers (EA-Ams) and European American youth and their mothers (Euro-Ams) rated the importance of life goals and reported upon their depressive symptoms, life satisfaction, and relationship quality. Results indicated that family and mate selection goals were more important for EA-Ams. Intergenerational similarity in achievement-related goals was higher for Euro-Ams whereas similarity in family-related goals was higher for EA-Ams. Intergenerational similarities in life goal priorities were differentially correlated with select outcomes.

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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.257
GPT teacher head0.410
Teacher spread0.153 · 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

Citations4
Published2015
Admission routes1
Has abstractyes

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