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Record W4387779596 · doi:10.1007/s11199-023-01429-y

Narratives of Children’s Gender Socialization from Fathers Who Take Parental Leave in South Korea

2023· article· en· W4387779596 on OpenAlexfundno aff
Youngcho Lee

Bibliographic record

VenueSex Roles · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicWork-Family Balance Challenges
Canadian institutionsnot available
FundersEconomic and Social Research CouncilCambridge TrustYork University
KeywordsSocializationPsychologyDevelopmental psychologyNarrativeSocial psychologyUndo

Abstract

fetched live from OpenAlex

Abstract Do leave-taking fathers who 'undo' gender in their division of domestic labour and responsibilities also ‘undo’ gender in relation to their children’s gender socialization? This exploratory qualitative study seeks to understand how leave-taking fathers in South Korea ( N = 17) experience and envision their children’s gender socialization by identifying three types of fathers. For ‘committed’ fathers, leave-taking is an extension of their genuine convictions and best efforts to raise children based on feminist ideals, but contradictory messages from non-parental influences such as preschools pose challenges. ‘Conflicted’ fathers undergo significant changes in their views about men and women’s roles through leave uptake but confess to still holding rigid views about children’s gender socialization. ‘Receptive’ fathers demonstrate more open and moderately flexible attitudes to children’s gender roles than the ‘conflicted’ fathers, although not as consciously, proactively, or consistently as the ‘committed’ fathers. The findings indicate that fathers’ uptake of leave leads to diverging, rather than uniform trajectories in fathers’ development of attitudes and behaviours toward children’s socialization. The findings point to the need to consider inconsistencies operating at multiple levels of the ‘gender trap,’ including between fathers’ attitudes toward adult and children’s gender roles, fathers’ behaviours and attitudes, and parental and non-parental influences.

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 score0.070
Threshold uncertainty score0.487

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.033
GPT teacher head0.294
Teacher spread0.261 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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