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Record W3124658769

Intergenerational Effects of Disability Benefits - Evidence from Canadian Social Assistance Programs

2013· preprint· en· W3124658769 on OpenAlexaboutno aff
Kelly Chen, Lars Osberg, Shelley Phipps

Bibliographic record

VenueScholar Works (Boise State University) · 2013
Typepreprint
Languageen
FieldSocial Sciences
TopicGender, Labor, and Family Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyCognitionAnxietyDevelopmental psychologyCognitive skillPsychiatry
DOInot available

Abstract

fetched live from OpenAlex

Individuals with disabilities face greater challenges in the labor market than able-bodied individuals and a growing body of research is finding that their children also tend to have more developmental problems than the children of able-bodied parents. Can transfer payments help reduce this gap? In this paper, we present the first evidence on how parental disability benefits affect the well-being of children. Using changes in real benefits under ten disability benefit programs in Canada as an identification strategy and Statistics Canada's National Longitudinal Survey of Children and Youth (NLSCY) as the data source on child outcomes, we find strong evidence that higher benefits lead to improvements in children's cognitive and non-cognitive development, as measured by math scores in standardized tests, hyperactive symptoms and emotional anxiety behavior. The effect is larger on children with a disabled mother than on those with a disabled father.

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)0.005
metaresearch head score (Gemma)0.016
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 score0.029
Threshold uncertainty score0.208

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.016
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.005
Science and technology studies0.0040.002
Scholarly communication0.0020.001
Open science0.0020.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.022
GPT teacher head0.243
Teacher spread0.221 · 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
Published2013
Admission routes1
Has abstractyes

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