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Record W4388731345 · doi:10.5209/tekn.86912

Adapting to motherhood: Online participation in WeChat groups to support first-time mothers

2023· article· en· W4388731345 on OpenAlexaff
Runxi Zeng, Hua Zhou, Richard Evans

Bibliographic record

VenueTeknokultura Revista de Cultura Digital y Movimientos Sociales · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Media and Politics
Canadian institutionsDalhousie University
Fundersnot available
KeywordsEthnographyAdaptation (eye)Identity (music)Social mediaSocial capitalPsychologySocial identity theorySocial psychologySocial groupSociologyComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

This study investigates how first-time mothers participate in online discussions in WeChat groups to support their adaptation to motherhood. Online ethnography and in-depth interviews are employed to examine the psychological and behavioural aspects of these first-time mothers within WeChat groups, as well as the group construction process. The study’s findings show that WeChat groups, formed around common identity, have integrated new media technology into the cultural practices of distinct social groups. Within these groups, first-time mothers establish unique information exchange networks with other first-time mothers, enabling the sharing of experiences, emotions, and resources, ultimately creating de facto identity communities. Moreover, WeChat groups serve as vital channels for acquiring and distributing social capital, expanding parenting resources and social networks. The study highlights the crucial role of WeChat groups in providing support to first-time mothers as they navigate motherhood while fostering a sense of camaraderie and belonging within this virtual community.

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)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.582
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.002

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 head0.076
GPT teacher head0.365
Teacher spread0.289 · 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.

Study designQualitative
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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