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Record W2116416786 · doi:10.1080/13636820.2014.958868

Youth apprenticeships in Canada: on their inferior status despite skilled labour shortages

2014· article· en· W2116416786 on OpenAlexaffabout
Wolfgang Lehmann, Alison Taylor, Laura Wright

Bibliographic record

VenueJournal of Vocational Education and Training · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicYouth Education and Societal Dynamics
Canadian institutionsUniversity of AlbertaWestern University
Fundersnot available
KeywordsApprenticeshipEconomic shortagePublic relationsVocational educationPolitical scienceEconomic growthQuality (philosophy)ScarcityPsychologyBusinessPedagogySociologyEconomics

Abstract

fetched live from OpenAlex

AbstractIn Canada, youth apprenticeships have been promoted as an educational alternative that leads to the development of valuable skills, allows for the opportunity to earn an income while learning and helps youth to gain a head start into lucrative, creative and in-demand careers. Yet, these programmes have remained rather marginal and continue to be perceived as being of lower-status compared to traditional post-secondary educational pathways, such as those leading to university or community college. In this paper, we draw on interview data with former youth apprentices in the province of Ontario to explore their reasons for entering apprenticeships in high school, their experiences in them and their own perceptions about the status and social recognition of apprenticeships. We suggest that policies regarding apprenticeship programmes in Canada need to expand their focus. While emphasis is currently placed on recruiting students by highlighting relatively utilitarian benefits, we argue that more focus needs to be placed on the training offered to apprentices including the commitment of employers to provide quality training on the job, the integration of classroom and on-the-job training and the opportunity for apprentices to move from partial to full participation in communities of practice.Keywords: apprenticeshipCanadastatus perceptionsyouthskilled tradeslabour shortages

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.357
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.038
GPT teacher head0.310
Teacher spread0.273 · 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 designQualitative
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

Citations26
Published2014
Admission routes2
Has abstractyes

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