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Record W3125879516

Disability Expenditures in Canada, 1970-1996: Trends, Reform Efforts and a Path for the Future

2001· article· en· W3125879516 on OpenAlexaffabout
Michele Campolieti, John N. Lavis

Bibliographic record

VenueSSRN Electronic Journal · 2001
Typearticle
Languageen
FieldSocial Sciences
TopicRetirement, Disability, and Employment
Canadian institutionsInstitute for Work & HealthThe Scarborough HospitalUniversity of Toronto
Fundersnot available
KeywordsPath (computing)Empirical researchPublic economicsEmpirical evidencePolitical scienceActuarial scienceEconomicsComputer science
DOInot available

Abstract

fetched live from OpenAlex

This paper reviews the programs - both public and private - that comprise the safety net for disabled persons in Canada. The paper has several objectives. First, we describe the trends in program expenditures in Canada between 1970 and 1996. Second, we discuss the plausible explanations for these trends and, where possible, the empirical evidence that establishes the relative importance of these explanations. Third, we discuss reform efforts implemented in the 1990s which seek to secure the financial viability of these programs. Finally, we discuss the need for additional program coordination and benefit integration in this system. If the support system for disabled persons in Canada is to move toward a more harmonized system in the future, it will be essential that these programs are examined and, if necessary, reformed as a single system rather than as separate or separable systems.

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)0.004
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 score0.100
Threshold uncertainty score0.725

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.014
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.038
GPT teacher head0.339
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

Citations1
Published2001
Admission routes2
Has abstractyes

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