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Record W7144705698

Material Custom in Changsha(A.D.235-237):Marriage and Age

2015· article· ja· W7144705698 on OpenAlexaboutno aff
Yuko WASHIO

Bibliographic record

VenueInstitutional Repositories DataBase (IRDB) · 2015
Typearticle
Languageja
FieldSocial Sciences
TopicChinese history and philosophy
Canadian institutionsnot available
Fundersnot available
KeywordsMarital statusPeriod (music)Life spanArranged MarriageQuarter (Canadian coin)Age at first marriageDeveloped countryPopulation
DOInot available

Abstract

fetched live from OpenAlex

This paper considers the marital customs in Linxiang County, Changsha Commandery, through examining bamboo strips named Liminbo that were unearthed in Zoumalou (Changsha, Hunan Province); the period under consideration is the fourth to sixth year of Jiahe in the Wu Kingdom during the Three Kingdoms Era.Since it is easier for us to determine whether women were married or not, the following aspects are considered: their average age of first marriage, whether or not all of them were married, whether remarrying was common, and until what age they remarried.The average age of first marriage of women in Linxiang County was fifteen to twenty years old, which corresponds with the opinion of previous studies. Moreover, unmarried women in their twenties and thirties were extremely rare, and the large majority married by their twenties.Furthermore, the average life span in this period was around forty to fifty years, and the marriage rate of women who were to be married to men of the above age does not decline in their thirties, and only starts to decline in their sixties. Women in this era remarried after their husbands died, and it was common for them to continue remarrying until they were in their fifties. The fact that women got married for the first time in their late teens is related to their reaching childbearing age. Moreover, it seems that women could get married at a relatively old age due to the importance of their role in maintaining the household; they contributed to productive labor such as farming and also carried out much of the housework.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.942
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.002
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 head0.051
GPT teacher head0.295
Teacher spread0.243 · 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 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
Published2015
Admission routes1
Has abstractyes

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