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Record W2598988568 · doi:10.3138/jcfs.39.3.371

The Importance of Social Context in the Formation of the Value of Children for Adolescents: Social Class and Rural Urban Differences in Taiwan

2008· article· en· W2598988568 on OpenAlexvenueno aff
Chin-Chun Yi, Hsiang-Ming Kung, Yu‐Hua Chen, Jou-Juo Chu

Bibliographic record

VenueJournal of Comparative Family Studies · 2008
Typearticle
Languageen
FieldPsychology
TopicCultural Differences and Values
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyValue (mathematics)Social value orientationsSocial classSocial environmentContext (archaeology)Developmental psychologySocial psychologySociologyDemographyGeographyPolitical scienceSocial science

Abstract

fetched live from OpenAlex

This paper examines how adolescents’ values may be shaped by the immediate social context with special reference to social class and rural urban background. The locus of study is Taiwan, a society with drastically declining birth rates in recent years. It is hypothesized that the value adolescents place on having children (or the positive value) and not having children (or the negative value) are accounted for by individual, familial and social contextual factors. Data are taken from an island-wide sample of first-year senior high students. A field survey was administered from winter of 2005 to spring of 2006. The analysis shows that three dimensions can be extracted from both positive and negative values toward having children: with emotional value rated the most important, followed by physical and social value. Results indicate that expected effects from different social class and rural-urban background vary and are salient for explaining physical and social value. In addition, social contextual factors and individual factors are shown to contribute to the formation of positive value of children among Taiwanese adolescents. The importance of social context in adolescents’ value formation with regard to the value of children is thus supported.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.243

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
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 head0.147
GPT teacher head0.388
Teacher spread0.241 · 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 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

Citations14
Published2008
Admission routes1
Has abstractyes

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