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Record W7096702545

POPULATION AND DEVELOPMENT REVIEW 33(4) : 757–783 (DECEMBER 2007) 757 The Decline of Son Preference in South Korea: The Roles of Development and Public Policy

2015· article· en· W7096702545 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicDemographic Trends and Gender Preferences
Canadian institutionsnot available
Fundersnot available
KeywordsPopulationChinaGovernment (linguistics)Public policyIdeologyPoliticsPreferenceQuarter (Canadian coin)
DOInot available

Abstract

fetched live from OpenAlex

ACROSS EAST ASIA and much of South Asia, child sex ratios have become more masculine in recent decades despite economic and social development and government efforts to induce parents to value daughters as highly as sons (Das Gupta et al. 2003, 2004). Figure 1 plots the trends in child sex ratios (the number of boys per 100 girls) in China, South Korea, and India since the 1950s. Only in Mao’s China and in North Korea has public policy demonstrably helped normalize national child sex ratios. In both settings an ideology of gender equity backed up by collectivization and control over private lives sharply reduced the scope for households to prioritize resource allocation among their members.1 More acceptable ways of reducing son preference are needed. Until a few years ago, South Korea appeared to epitomize the pattern of rising sex ratios despite rapid development. Dramatic increases occurred in the country’s levels of education, industrialization, and urbanization, as well as in women’s education and participation in the formal labor force (see Table 1). By the mid-1990s, South Korea was officially included as a member of the developed countries ’ club, the OECD. Yet sex ratios at birth rose steeply during this period.2 This rise in sex ratios flew in the face of more than a century of social science theory. Early theorists focused on the profound cultural and be-havioral implications of the shift from preindustrial to industrial economic organization.3 Essentially, this involved a shift from localized communities bound by religious and other traditions to more complex and impersonal social groupings characterized by contractual associations. Accompanying

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.037
Threshold uncertainty score0.123

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.010
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0370.011

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 head0.131
GPT teacher head0.334
Teacher spread0.203 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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