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Record W2031822629 · doi:10.1111/1467-8454.00203

Intergenerational Welfare Participation in New Zealand

2003· article· en· W2031822629 on OpenAlexaboutno aff
Tim Maloney, Sholeh A. Maani, Gail Pacheco

Bibliographic record

VenueAustralian Economic Papers · 2003
Typearticle
Languageen
FieldSocial Sciences
TopicIntergenerational and Educational Inequality Studies
Canadian institutionsnot available
Fundersnot available
KeywordsWelfareQuarter (Canadian coin)Welfare dependencyInstrumental variableEducational attainmentDemographic economicsCorrelation coefficientEstimationSocial WelfarePanel dataEconomicsEconometricsStatisticsMathematicsEconomic growthGeographyPolitical science

Abstract

fetched live from OpenAlex

New Zealand panel data, which provide extensive information on the benefit histories of parents and their children, are used to estimate an intergenerational correlation coefficient in social welfare dependency. Recent estimation techniques for addressing issues of measurement error are applied to this analysis. The long‐term benefit histories of parents and instrumental variable techniques provide useful lower and upper‐bound estimates of the true intergenerational correlation. Our results suggest that the true correlation coefficient between the welfare participation of parents and their offspring is somewhere between one‐third and two‐thirds, but probably much closer to the lower limit in this range. Approximately one‐quarter of this effect appears to operate through the lower educational attainment of children reared in families receiving social welfare benefits.

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)0.003
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 score0.684
Threshold uncertainty score0.635

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.064
GPT teacher head0.365
Teacher spread0.301 · 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

Citations20
Published2003
Admission routes1
Has abstractyes

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