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Record W3121447473

Earnings Supplements and Job Quality Among Former Welfare Recipients: Evidence from the Self-Sufficiency Project

2003· article· en· W3121447473 on OpenAlexaff
Kelly Foley, Saul Schwartz

Bibliographic record

VenueSSRN Electronic Journal · 2003
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Labor, and Family Dynamics
Canadian institutionsCarleton UniversityUniversity of British ColumbiaUniversity of British Columbia Hospital
Fundersnot available
KeywordsEarningsWelfareLabour economicsDemographic economicsSelf-employmentSocial assistanceQuality (philosophy)Quality circleBusinessEconomicsOperations managementEconomic growthAccounting
DOInot available

Abstract

fetched live from OpenAlex

The Self-Sufficiency Project (SSP) offered a generous but time-limited earnings supplement to a randomly assigned group of lone parents—who were also long-term social assistance recipients—if they found full-time work and left social assistance. Employment data was collected for this group over a three-year period following the offer, and for a randomly-assigned control group. This article analyzes the characteristics of the first job that SSP participants found after they left social assistance. The occupations and industries of the first job held are analyzed as is SSP’s impact on hourly wages, weekly hours and job stability. The article finds that SSP increased employment in jobs that were no worse (and no better) than the jobs that participants might have taken in the absence of the program.

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)0.003
metaresearch head score (Gemma)0.008
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 score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.024
GPT teacher head0.314
Teacher spread0.290 · 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

Citations0
Published2003
Admission routes1
Has abstractyes

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