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Record W2942812774 · doi:10.3386/w22094

Three-generation Mobility in the United States, 1850-1940: The Role of Maternal and Paternal Grandparents

2016· preprint· en· W2942812774 on OpenAlexaff
Claudia Olivetti, M. Daniele Paserman, Laura Salisbury

Bibliographic record

VenueNational Bureau of Economic Research · 2016
Typepreprint
Languageen
FieldSocial Sciences
TopicIntergenerational and Educational Inequality Studies
Canadian institutionsYork University
Fundersnot available
KeywordsGrandparentTraitInheritance (genetic algorithm)Demographic economicsMatching (statistics)DemographyPsychologyEconomicsDevelopmental psychologyMedicineSociologyBiologyGenetics

Abstract

fetched live from OpenAlex

This paper estimates intergenerational elasticities across three generations in the United States in the late 19th and early 20th centuries.We extend the methodology in Olivetti and Paserman (2015) to explore the role of maternal and paternal grandfathers for the transmission of economic status to grandsons and granddaughters.We document three main findings.First, grandfathers matter for income transmission, above and beyond their effect on fathers' income.Second, the socio-economic status of grandsons is influenced more strongly by paternal grandfathers than by maternal grandfathers.Third, maternal grandfathers are more important for granddaughters than for grandsons, while the opposite is true for paternal grandfathers.We present a model of multitrait matching and inheritance that can rationalize these findings.

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.078
Threshold uncertainty score0.154

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.370
GPT teacher head0.514
Teacher spread0.144 · 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

Citations1
Published2016
Admission routes1
Has abstractyes

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