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Record W3190271966 · doi:10.5539/ass.v17n8p38

An Investigation on the Legislative Tradition of Should Be Rough Rather Than Detailed in China's Marriage Law—Also on the Early Practice of Chinese Feminist Movement

2021· article· en· W3190271966 on OpenAlexvenueno aff
Yining Hou

Bibliographic record

VenueAsian Social Science · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicDemographic Trends and Gender Preferences
Canadian institutionsnot available
Fundersnot available
KeywordsLegislationLawLegislatureMarriage lawPolitical scienceCivil law (Civil law)VictoryPromulgationFamily lawCommon lawChinese lawSociologyPublic lawMunicipal lawPolitics

Abstract

fetched live from OpenAlex

The Civil Code of the People's Republic of China, which was reviewed and passed in May 2020, continues the tradition of should be rough rather than detailed in China's marriage and family legislation since the promulgation of the Marriage Law of 1950. The embodiment of this tradition in the marriage law text is fewer legal provisions, more general provisions, and more moral norms. This legislative tradition did not come from the Soviet Union but was mainly due to the unique legal nature of the Marriage Law of 1950. This law is the product of the victory of the Chinese feminist movement since the Revolution of 1911. Its legislative tradition of should be rough rather than detailed is determined by the legislative purpose of this law to break the feudal marriage system and protect the rights of women and children, and its unique legal attributes. The formulation of this law was subject to the legislative difficulties and the social reality faced by the marriage law drafting group centered on women leaders. It was a helpless choice in a particular period. In the modern society where China's economy, society, and marriage and family relations have undergone significant changes, it is crucial to improve marriage legislation and reverse the legislation tradition of should be rough rather than detailed, so that the improvement of legislative techniques and legislative goals meet the needs of economic and social development.

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)0.010
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.066
Threshold uncertainty score0.132

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0080.008
Scholarly communication0.0040.004
Open science0.0010.001
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0020.000

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 head0.080
GPT teacher head0.347
Teacher spread0.267 · 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 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

Citations0
Published2021
Admission routes1
Has abstractyes

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