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Record W3167691247 · doi:10.3390/jrfm14060265

Examining Low-Income Single-Mother Families’ Experiences with Family Benefit Packages during and after the Great Recession in the United States

2021· article· en· W3167691247 on OpenAlexvenueno aff
Yu‐Ling Chang, Chi‐Fang Wu

Bibliographic record

VenueJournal of risk and financial management · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Labor, and Family Dynamics
Canadian institutionsnot available
FundersInstitute for Research on Labor and Employment
KeywordsRecessionWelfarePovertySubsidyIncome SupportSingle mothersEconomicsLow incomeSocial securityWork (physics)Great recessionEconomic mobilityCashDemographic economicsBusinessLabour economicsEconomic growthFinancePsychologyMacroeconomics

Abstract

fetched live from OpenAlex

The recent economic recession triggered by the global pandemic has renewed scholarly interest in the role of social welfare systems in supporting economically vulnerable families when they experience employment instability. This article unpacks the patterns of the cash and in-kind components of the monthly family benefit packages that US low-income single mothers accessed during and after the Great Recession. We used the 2008 Survey of Income and Program Participation and an innovative analytic procedure involving family benefit package plots, group-based trajectory modeling, and logistic regression modeling. We found that low-income single mothers more often used in-kind basic-needs packages and less often used packages that bundle a cash benefit or a childcare subsidy, regardless of their dynamic employment status. Our findings challenge the effectiveness of the US work-based welfare system in ensuring the economic security of economically vulnerable families and contribute to the policy discussions on unconditional basic income and President Biden’s American Families Plan.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.047
Threshold uncertainty score0.358

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.012
GPT teacher head0.230
Teacher spread0.219 · 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.

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

Citations8
Published2021
Admission routes1
Has abstractyes

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