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Record W2515439570 · doi:10.3138/cpp.2015-077

A Longitudinal Analysis of GIS Entries and Exits

2016· article· en· W2515439570 on OpenAlexaffvenueabout
Ross Finnie, David Gray, Yan Zhang

Bibliographic record

VenueCanadian Public Policy · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicRetirement, Disability, and Employment
Canadian institutionsStatistics CanadaUniversity of Ottawa
Fundersnot available
KeywordsReceiptDemographic economicsDuration (music)HazardHazard modelSpellEconometricsMarital statusDemographyActuarial scienceGeographyEconomicsSociology

Abstract

fetched live from OpenAlex

We focus on one particular pillar of the public retirement income network in Canada, namely, receipt outcomes of the Guaranteed Income Supplement (GIS) regime. This empirical analysis is carried out in a dynamic framework. We address the extent to which individuals enter the state of GIS receipt at various ages as well as the extent to which individuals who receive GIS benefits at the earliest age of eligibility subsequently exit the regime. We first measure these transition rates, and then we focus our analysis primarily on the impact of three attributes of recipients: changes in marital status, entry cohort, and current age. The econometric equations include simple transition models of both entries and exits, as well as hazard models of the probability of exit. Among our many empirical findings is a non-trivial incidence of delayed entry into the regime as well as exit from the regime conditional on prior receipt of benefits. Women who transit from married to single status are more likely to enter, but the opposite finding is discerned for men. The hazard model for the risk of exiting the GIS regime conditioned on the duration of the ongoing spell of receipt reveals a sharp pattern of negative duration dependence.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.246
Threshold uncertainty score0.877

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.187
GPT teacher head0.405
Teacher spread0.218 · 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

Citations3
Published2016
Admission routes3
Has abstractyes

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