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Record W1492780117 · doi:10.1086/648658

Intergenerational Wealth Transmission among Agriculturalists

2010· article· en· W1492780117 on OpenAlexaff
Mary K. Shenk, Monique Borgerhoff Mulder, Jan Beise, Gregory Clark, William Irons, Donna L. Leonetti, Bobbi S. Low, Samuel Bowles, Tom Hertz, Adrian V. Bell, Patrizio Piraino

Bibliographic record

VenueCurrent Anthropology · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicRangeland Management and Livestock Ecology
Canadian institutionsStatistics Canada
Fundersnot available
KeywordsInequalityAgricultureEmbodied cognitionSocial inequalityTransmission (telecommunications)National wealthEconomicsDevelopment economicsGeography

Abstract

fetched live from OpenAlex

This paper uses data from eight past and present societies practicing intensive agriculture to measure the transmission of wealth across generations in preindustrial agricultural societies. Focusing on embodied, material, and relational forms of wealth, we compare levels of wealth between parents and children to estimate how effectively wealth is transmitted from one generation to the next and how inequality in one generation impacts inequality in the next generation. We find that material wealth is by far the most important, unequally distributed, and highly transmitted form of wealth in these societies, while embodied and relational forms of wealth show much weaker importance and transmission. We conclude that the unique characteristics of material wealth, and especially wealth in land, are key to the high and persistent levels of inequality seen in societies practicing intensive agriculture. We explore the implications of our findings for the evolution of inequality in the course of human history and suggest that it is the intensification of agriculture and the accompanying transformation of land into a form of heritable wealth that may allow for the social complexity long associated with agricultural societies.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.183
Threshold uncertainty score0.968

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0330.001

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 head0.009
GPT teacher head0.271
Teacher spread0.262 · 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 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

Citations113
Published2010
Admission routes1
Has abstractyes

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