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Record W2965224933 · doi:10.5539/ass.v15n8p104

Research on the Levirate Marriage for the Han Chinese during Yuan Dynasty

2019· article· en· W2965224933 on OpenAlexvenueno aff
Zongyan Jiang

Bibliographic record

VenueAsian Social Science · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicChinese history and philosophy
Canadian institutionsnot available
Fundersnot available
KeywordsChinaJurisdictionIdeologyState (computer science)Position (finance)Marriage lawLawPolitical scienceChinese lawHan chineseHistory of ChinaHistoryAncient historyCommon lawMunicipal lawBusiness

Abstract

fetched live from OpenAlex

The levirate marriage has been continued for thousands of years in the ancient time of China until after the People's Republic when it died out. In Yuan Dynasty, the levirate marriage was gradually ascended to state law from national habits; its position was so important that it was considered as the customs of the country, later it gradually moved towards collapse. The variation of laws as well as regulations for the levirate marriage reflect the process of which the two cultures of the Mongolian people and Han people blended continuously. Starting from the research on the policy of the levirate marriage for the Han Chinese, this article explores for the jurisdiction and control of rulers in Yuan Dynasty for the Han Chinese to disclose the ideological features of laws in Yuan Dynasty, which explore for the conflict and coordination of law culture in grassland of the law culture in central plains.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.002
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.066
GPT teacher head0.388
Teacher spread0.322 · 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 designNot applicable
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
Published2019
Admission routes1
Has abstractyes

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