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Record W2086536497 · doi:10.1080/13596748.2010.526796

Work‐based learning in Canada and the United Kingdom: a framework for understanding knowledge transfer for workers with low skills and higher skills

2010· article· en· W2086536497 on OpenAlex
Maurice Taylor, Karen Evans, Christine Pinsent‐Johnson

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueResearch in Post-Compulsory Education · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education and Employability
Canadian institutionsUniversity of Ottawa
FundersEconomic and Social Research Council
KeywordsWorkforceVariety (cybernetics)Work (physics)Knowledge transferExplanatory powerWork-based learningPsychologyKnowledge managementMedical educationPedagogyMathematics educationComputer sciencePolitical scienceEngineeringMedicineArtificial intelligence

Abstract

fetched live from OpenAlex

The purpose of this study was to investigate how knowledge of different kinds is put to work in workplace education programmes for adults with low skills and adults with higher skills. A novel framework, which has moved beyond narrow conceptions of ‘transfer’ to concentrate on different forms of knowledge and the ways these are recontextualised as people move between sites of learning, was used to examine three work‐based programmes in Canada. The aim was to compare the Canadian results with those obtained from six work‐based exemplar programmes previously analysed according to the framework in the United Kingdom. Results indicate that the seven elements in the framework hold some explanatory power across a wide variety of workforce upskilling programmes as well as across learner skill levels and at the same time help to focus in on four kinds of knowledge recontextualisations.

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.

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)0.004
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.342
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.078
GPT teacher head0.408
Teacher spread0.330 · 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