Contemporary Apprenticeship: International Perspectives on an Evolving Model of Learning
Bibliographic record
Abstract
Throughout the world, people understand the meaning of 'apprenticeship'. As a model of learning and skill formation, apprenticeship has adapted over the years to reflect changes in work, in technology, and in the types of knowledge that underpin occupational expertise. Apprenticeship serves the needs of government, as well as employers, individuals and society more generally. These needs have always co-existed in dynamic tension. This book explores the contemporary state of apprenticeship in Europe, the United States, Canada, and Ghana. The chapters present perspectives from leading researchers in the field, showing how apprenticeship is evolving and changing in every country (crossing boundaries of age, sector and levels of skill and knowledge) and examining the ability of apprenticeship to facilitate both vertical progression – particularly to higher education – and horizontal progression between jobs and sectors. As such, apprenticeship remains at the core of debates about vocational learning and the nature of expertise. This book was originally published as a special issue of the Journal of Vocational Education and Training.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.019 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".