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Record W3122031421

Financial Capability in the United States: Consumer Decision-Making and the Role of Social Security

2010· preprint· en· W3122031421 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

VenueDeep Blue (University of Michigan) · 2010
Typepreprint
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Literacy, Pension, Retirement Analysis
Canadian institutionsnot available
FundersAustralian GovernmentU.S. Social Security Administration
KeywordsQuarter (Canadian coin)Social securityFinancial statementBusinessDebtPopulationFinanceCurrent Population SurveyPaymentActuarial sciencePublic economicsEconomicsAccounting
DOInot available

Abstract

fetched live from OpenAlex

This paper analyzes new data from the 2009 National Financial Capability Study. This survey provides information to assess how American households make financial decisions, how they are faring under current economic conditions, and in what ways financial knowledge contributes to financial capability. In addition, it includes data about the information that the Social Security Administration (SSA) provides to consumers. The paper finds that the majority of individuals do not plan for retirement or make provisions against shocks. Debt management often results in sizable interest payments and fees and it is notable how many individuals have used high-cost methods of borrowing in the past five years. Levels of financial knowledge are strikingly low and many respondents do not possess knowledge of basic concepts. Social Security has taken
\nsteps to provide information about what individuals will expect to receive when they retire. The self-reported evidence provided in the survey shows that the information has been used by about a quarter of the population who acknowledge receiving the statement. Moreover, there are large
\ndifferences among use in demographic groups and some of the more vulnerable populations,
\nsuch as African-Americans, those hit by shocks, and single and separated individuals are more likely to use the statement.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.482
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.005
GPT teacher head0.197
Teacher spread0.191 · 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