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Record W2132027742

Who's missing out on the GIS?

2005· article· en· W2132027742 on OpenAlexaffabout
Preston Poon

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldDecision Sciences
Topicdemographic modeling and climate adaptation
Canadian institutionsStatistics Canada
Fundersnot available
KeywordsPaymentCensusLow incomeSocial securityPensionGeographyBusinessDemographic economicsSocioeconomicsEconomicsDemographyPopulationFinanceSociology
DOInot available

Abstract

fetched live from OpenAlex

The 2005 federal budget directed morespending to help low-income seniors byincreasing Guaranteed Income Supplement (GIS) payments by roughly $2.7 billion (more than $400 per year for a single senior and almost $700 per couple for those receiving the maximum). The GIS was established in 1967 as an additional benefit to low-income seniors receiving Old Age Security (OAS). These programs, plus the maturation of the Canada and Quebec Pension Plans (C/QPP) and an increased use of private pension plans have reduced low income among seniors significantly over the past decade (Myles 2000). In 1980, roughly 1 in 5 seniors were in low income; by 2003 this had fallen to 1 in 15.1 According to the 2001 Census, seniors living in low income re-ceived two-thirds of their income from OAS and GIS benefits. An additional 20 % came from C/QPP.

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)0.008
metaresearch head score (Gemma)0.040
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.079
Threshold uncertainty score0.264

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.040
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0060.004
Scholarly communication0.0090.014
Open science0.0020.004
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0790.028

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 head0.249
GPT teacher head0.408
Teacher spread0.158 · 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

Citations7
Published2005
Admission routes2
Has abstractyes

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