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Record W2142345842 · doi:10.1111/1475-4932.12222

Disability and Multi‐State Labour Force Choices with State Dependence

2015· article· en· W2142345842 on OpenAlexaff
Umut Oguzoglu

Bibliographic record

VenueEconomic Record · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicRetirement, Disability, and Employment
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMultinomial logistic regressionShock (circulatory)EconomicsWork (physics)Demographic economicsLimitingLogitLabour economicsBritish Household Panel SurveyPanel Study of Income DynamicsState dependentState (computer science)EconometricsMultinomial distributionMedicineMathematicsStatisticsPhysics

Abstract

fetched live from OpenAlex

Using the first 12 waves of the Household, Income and Labour Dynamics in Australia survey, the impact of work‐limiting disabilities on disaggregated labour force choices is investigated. Findings from a dynamic mixed multinomial logit model point to a significant decline in the likelihood of full‐time and part‐time work due to disability. Combined with strong state dependence in employment choices, this suggests a slow recovery from a disability shock. Model simulations suggest that high cross‐ and own‐state dependence can amplify a one‐off disability shock to alter the probability of full‐time employment and non‐participation over a long period, especially for low‐skilled individuals.

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)0.003
metaresearch head score (Gemma)0.011
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 score0.035
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0100.000

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 head0.156
GPT teacher head0.386
Teacher spread0.230 · 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

Citations15
Published2015
Admission routes1
Has abstractyes

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