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Record W2105385932 · doi:10.1017/s026841600400493x

Introduction: Women, property and legal change

2004· article· en· W2105385932 on OpenAlexaff
Kris Inwood

Bibliographic record

VenueContinuity and Change · 2004
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHistorical and socio-economic studies of Spain and related regions
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsIngenuityVariety (cybernetics)Property (philosophy)Inheritance (genetic algorithm)Meaning (existential)Context (archaeology)Law and economicsProperty rightsCultural propertySociologyDistribution (mathematics)Political scienceLawEpistemologyEconomicsGeographyNeoclassical economicsCultural heritage

Abstract

fetched live from OpenAlex

The distribution of property in any community reflects a variety of cultural, economic and social influences including, not least, a legal framework that defines property and the rights to use and transfer it. Legal change, in turn, has the capacity to reshape distribution and the complex matrix of culture, society and economy that surrounds access to property. The articles that have been brought together for this special issue of Continuity and Change document and analyse the gendered patterns of ownership in a variety of times and places. Each of the contributors has encountered some difficulty in determining the relevant legal framework, the extent to which laws were enforced, who owned what, patterns of inheritance and the meaning of ownership itself. The authors respond by examining a range of sources with imagination, ingenuity and methodologies that originate in different disciplines. The precise shape of the research of course depends on the circumstances of the particular historical context.

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)0.003
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.033
Threshold uncertainty score0.111

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.005
Scholarly communication0.0050.003
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0330.004

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 head0.044
GPT teacher head0.197
Teacher spread0.152 · 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

Citations2
Published2004
Admission routes1
Has abstractyes

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