Convergence in education policy : Higher vocational education as a strategic response to the challenges of the knowledge-based economy in coordinated and liberal market economies
Bibliographic record
Abstract
This thesis approaches the intersection of education and welfare from a social policy perspective with a particular focus on current socio-economic transformations towards a knowledge-based economy. The research interest is centred on the question of how different market economies react to these transformations. To narrow down the topic, this thesis focuses on the development of higher vocational education (i.e., vocational education at tertiary level, such as dual study programs, applied degrees, or professional degrees). \nUsing Varieties of Capitalism as theoretical foundation, it is assumed that both liberal and coordinated market economies rely on an expansion of higher vocational education in order to cope with the challenges of the transformation towards a knowledge-based economy. However, the reasons for this convergence are different for each system type. Coordinated market economies traditionally rely on (secondary level) vocational education and training and have relatively low numbers of higher education graduates. Due to the increased skill demand in knowledge-based economies – even within intermediate skill sectors –, these economies are increasing the share of higher vocational education. Liberal market economies, on the other hand, face a skill deficit in the intermediate tier due to the historic decline of apprenticeship programs and therefore a current lack of vocational education. To increase the level of education focused on intermediate skills, it is expected that these economies are also developing and expanding higher vocational education. Thus, the research question for this thesis is: Which policy strategies do governments in liberal and coordinated market economies implement in regard to higher vocational education to cope with the transition towards a knowledge-based economy? \nA qualitative research design is used: The two cases of Canada (the provinces of Ontario and British Columbia) and Austria, representing each type of economy according to the Varieties of Capitalism theory, are investigated. The analysis consists of two distinct parts: (1) Secondary sources are used to determine if higher vocational education was expanded, and (2) a qualitative content analysis of legislature is used to gain insight into the governments’ strategies behind this development. The results of the analyses show that higher vocational education is expanded in Ontario and Austria, while the expansion is less clearly observable in British Columbia. The narrative of expanding higher vocational education due to the development of the knowledge-based economy is not explicitly used as a government strategy in Austria and Ontario, and it plays a minor role in British Columbia.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".