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Record W2129944506 · doi:10.1186/2193-9004-3-10

The effect of vocational rehabilitation on the employment outcomes of disability insurance beneficiaries: new evidence from Canada

2014· article· en· W2129944506 on OpenAlexafffundabout
Michele Campolieti, Morley Gunderson, Jeffrey A. Smith

Bibliographic record

VenueIZA Journal of Labor Policy · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicRetirement, Disability, and Employment
Canadian institutionsCentre for Social InnovationThe Scarborough HospitalUniversity of Toronto
FundersNational Institute on Disability and Rehabilitation ResearchSocial Sciences and Humanities Research Council of CanadaU.S. Department of Education
KeywordsPensionMatching (statistics)Selection (genetic algorithm)Disability insuranceWeightingInstrumental variableActuarial scienceVocational educationIdentification (biology)RehabilitationEconomicsSocial insuranceEstimatorSocial policyAverage treatment effectEconometricsPsychologySocial securityComputer scienceStatisticsMedicineEconomic growthFinanceMathematics

Abstract

fetched live from OpenAlex

Abstract We estimate the effects of the vocational rehabilitation (VR) program run by the Canada Pension Plan Disability Program using administrative data. Identification relies on “selection on observed variables” plus careful comparison group selection and institutional knowledge regarding sources of conditional variation in participation. We employ several matching and weighting estimators and emphasize flexible conditioning on variables suggested by theory, the institutional setup and the literature. We find modest, and imprecisely estimated, impacts on employment outcomes for men and larger, sometimes statistically significant, impacts for women. A formal sensitivity analysis finds our results are quite robust to lingering selection on unobserved variables. JEL codes I38; J08; J24

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)0.004
metaresearch head score (Gemma)0.013
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 score0.027
Threshold uncertainty score0.199

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.007
Science and technology studies0.0020.001
Scholarly communication0.0020.000
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.068
GPT teacher head0.400
Teacher spread0.331 · 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

Citations21
Published2014
Admission routes3
Has abstractyes

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Same venueIZA Journal of Labor PolicySame topicRetirement, Disability, and EmploymentFrench-language works237,207