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Record W2141162030

The Apprenticeship System in Canada: Trends and Issues

2005· article· en· W2141162030 on OpenAlexaboutno aff
Andrew Sharpe, James Gibson

Bibliographic record

VenueCSLS Research Reports · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicEducation Systems and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsApprenticeshipIncentiveBusinessQuality (philosophy)Labour economicsEconomicsMarket economy
DOInot available

Abstract

fetched live from OpenAlex

This report provides an overview of the trends and issues related to the apprenticeship system in Canada. It first discusses theoretical perspectives on apprenticeship, then reviews the institutional features of apprenticeship systems in Canada and in other countries. The report concludes that the market for apprenticeship is principally constrained by employer demand rather than by the supply of potential apprentices. Consequently, it proposes reforms based on three main principles: apprenticeship programs should focus on improving the quality rather than the quantity of potential apprentices; financial incentives should be primarily directed towards firms; and strong apprenticeship sectoral committees are important in improving apprenticeship training and helping employers make investments in apprentices.

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)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.120
Threshold uncertainty score0.868

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0080.019
Science and technology studies0.0070.003
Scholarly communication0.0070.002
Open science0.0030.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.131
GPT teacher head0.475
Teacher spread0.344 · 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 designNot applicable
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

Citations36
Published2005
Admission routes1
Has abstractyes

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