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Record W2158268180

Older Workers and On-the-Job Training in Canada: Evidence from the WES Data

2007· preprint· en· W2158268180 on OpenAlexaboutno aff
Işık U. Zeytinoglu, Gordon B. Cooke, Karlene Harry

Bibliographic record

VenueRePEc: Research Papers in Economics · 2007
Typepreprint
Languageen
FieldSocial Sciences
TopicRetirement, Disability, and Employment
Canadian institutionsnot available
Fundersnot available
KeywordsTraining (meteorology)Economic shortageJob trainingLogistic regressionPsychologyJob satisfactionMedicineDemographic economicsSocial psychologyGeographyEconomicsVocational education
DOInot available

Abstract

fetched live from OpenAlex

This paper provides evidence of on-the-job training among older workers in Canada. It also examines the effect of age associated with on-the-job training. Statistics Canada’s Workplace and Employee Survey (WES) 2001 data, linking employee responses to workplace (i.e. employer) responses are used. Three quarters of workers are categorized as middle aged, with about one in ten being younger and one in five considered to be older. Only 32% of Canadian workers received on-the-job training in the year preceding this survey. When separating workers into the three age categories, 37%, 34%, and 24% of younger, middle-aged, and older workers, respectively, received on-the-job training in that year. Logistic regression analysis results showed that, controlling for workplace, job and individual factors, as compared to middle-aged workers, older workers are significantly less likely to receive on-the-job training. The lack of on-the-job training for older workers should be a concern for policy makers at a time when labour shortages are being predicted. Older workers are healthier than ever and the provision of on-the-job training should be encouraged to retain older workers in the labour market in Canada.

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)0.011
metaresearch head score (Gemma)0.003
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 score0.266
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0020.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.447
GPT teacher head0.463
Teacher spread0.016 · 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

Citations5
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueRePEc: Research Papers in EconomicsSame topicRetirement, Disability, and EmploymentFrench-language works237,207