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Inheritance: A Gendered and Intergenerational Dimension of Poverty

2012· article· en· W2081017318 on OpenAlexaff
Elizabeth Cooper, Kate Bird

Bibliographic record

VenueDevelopment Policy Review · 2012
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing, Finance, and Neoliberalism
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsInheritance (genetic algorithm)PovertySpouseAsset (computer security)Dimension (graph theory)SociologyEconomicsEconomic growthDemographic economicsDevelopment economicsBiology

Abstract

fetched live from OpenAlex

This collection of articles contains new and important findings concerning the scale and significance of asset transfers through inheritance among different populations, as well as the ways in which inheritance affects economic and social status and mobility. Evidence exists of women commonly losing access to assets when properties are redistributed following a spouse's death. This and the household effects of gaining or losing access to heritable property highlight the gendered and intergenerational dimensions of inheritance. As an introduction to the collection, this article provides an overview of how inheritance has been understood in poverty‐related policy and research up to now. We then synthesise what the new findings presented in this collection tell us about inheritance as a crucial factor in women's poverty and the intergenerational transmission of poverty, highlighting what other researchers and policy‐makers can take from this research to address the gendered and intergenerational dimensions of inheritance in different contexts.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.003
Scholarly communication0.0020.003
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.061
GPT teacher head0.271
Teacher spread0.210 · 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 designObservational
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

Citations47
Published2012
Admission routes1
Has abstractyes

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