Tertiary education as social policy: Comparative theoretical perspectives
Bibliographic record
Abstract
Abstract In this paper, we conduct an in-depth review of and commentary on two frameworks for international comparative work focused on education systems and skill formation – specifically, welfare regime and production regime approaches. We focus on how tertiary education is understood to function relationally within a national policy repertoire and explore the interplay between education and economic systems. Whereas the welfare regime literature illuminates why some regimes are conducive to human capital production and create more equitable educational and labour market opportunities, the production regime literature focuses on the ways that actors such as government, educational institutions, and unions optimise skill formation. These two theoretical perspectives offer both rival and complementary explanations of varying patterns in public investment, differentiation in education systems, and participation rates in tertiary education across countries. Our analytical account provides useful insights for understanding different national education policies and framing future research, including informing these perspectives with the more recent theoretical contributions of the social investment approach. In relation to changing conceptions of the knowledge economy, education, skill development, and the nature of employment, these two theoretical perspectives continue to provide useful conceptual lenses to examine the education/skill /employment nexus.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.004 | 0.028 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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 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".