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Record W2063918570 · doi:10.1177/0022185608099667

On-the-Job Training in Canada: Associations with Information Technology, Innovation and Competition

2009· article· en· W2063918570 on OpenAlexaffabout
Işık U. Zeytinoglu, Gordon B. Cooke

Bibliographic record

VenueJournal of Industrial Relations · 2009
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicLabor market dynamics and wage inequality
Canadian institutionsMemorial University of NewfoundlandMcMaster University
Fundersnot available
KeywordsProsperityCompetition (biology)Training (meteorology)BusinessInformation technologyJob satisfactionMarketingEconomic growthPolitical scienceManagementEconomicsGeography

Abstract

fetched live from OpenAlex

This article focuses on the associations between on-the-job training and new information technology, innovation introduced in the workplace, and competition experienced by the workplace. The study uses Statistics Canada's 2001 Workplace and Employee Survey, a Canada-wide survey of employers and employees. Only about a third of Canadian workers receive on-the-job training. Multivariate results show that innovation introduced in the workplace is significantly associated with providing on-the-job training. To a lesser extent, implementing new information technology and experiencing competition are also positively associated with on-the-job training. Economic growth and prosperity as well as inclusion and equality can be achieved by providing opportunities for workers to learn and develop their skills and abilities. We recommend governments to support workplaces and workers in their initiatives for the broader-focused on-the-job training since it is a social good that will benefit the society as well as the workers and their workplaces.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.168
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.041
GPT teacher head0.217
Teacher spread0.177 · 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 designTheoretical or conceptual
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
Published2009
Admission routes2
Has abstractyes

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