MétaCan
Menu
Back to cohort
Record W2555266712 · doi:10.7758/rsf.2016.2.6.01

How Wealth Inequality Shapes Our Future

2016· article· en· W2555266712 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueRSF The Russell Sage Foundation Journal of the Social Sciences · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Literacy, Pension, Retirement Analysis
Canadian institutionsnot available
FundersEunice Kennedy Shriver National Institute of Child Health and Human Development
KeywordsNet worthPovertyNational wealthEconomicsDebtInequalityWhite (mutation)Net incomeQuarter (Canadian coin)Distribution (mathematics)Demographic economicsInflation (cosmology)Labour economicsFinanceHistoryEconomic growth

Abstract

fetched live from OpenAlex

Liz, Mary, and Howard are three teenagers in the 1980s. Although unrelated, their families have much in common: stable two- parent households, at least one parent completed high school (though none of them went to college), and all three are white. They differ in one important aspect: their parents command quite different levels of wealth (here measured as net worth, that is, the total sum of financial and real assets minus debt). Liz's parents own less than $700 (inflation adjusted to 2013 dollars), meaning that Liz grows up at the bottom of the wealth distribution. Still, she is far from living in poverty thanks to her parents' annual income of about $50,000. Mary's parents have a somewhat higher income, about $70,000, but also markedly more wealth than Liz's parents: their net worth of roughly $60,000 puts them at about the national median of the time. Also unlike Liz's parents, they are homeowners. Howard is lucky enough to grow up in affluence. Not in terms of income, given that his parents have a household income of only about $40,000, but they have considerable wealth. With a net worth of nearly a quarter million dollars, Howard's parents are in the top 20 percent of wealth holders. They, too, own their home.

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.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.337
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0010.002
Open science0.0010.000
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 head0.030
GPT teacher head0.288
Teacher spread0.258 · 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