Students, markets and social justice higher education fee and student support policies in Western Europe and beyond
Bibliographic record
Abstract
This volume examines tuition fees as the most prominent and most visible trend among higher education policies that embodies recent neo-liberal trends in the policy area of education. Tuition fee policies and the accompanying provisions for student support illustrate the contemporary tensions between marketisation and social justice. Among the major transformations higher education systems have undergone in the last two decades, the emergence of marketisation, and in particular the introduction of tuition fees, has received a lot of attention. In Europe, these trends seemingly break with a long-dominant representation of higher education as a public good, which has been at the centre of the process of massification of higher education access in most European countries since the 1960s. Against this background, the volume examines recent changes in tuition fee policies in a number of Western European countries, Canada, the USA and China, and investigates the impacts of these changes on access to higher education. There are two main contributions the volume makes: first, it provides an overview of recent reforms in a comparative perspective, including a diverse range of national contexts; second, it elaborates a systematic analysis of tuition fee policies' rationales, instruments and outcomes in terms of access to higher education. The volume argues that tuition fee policies provide fruitful grounds to explore the variety of neo-liberal trends in higher education - that is, how marketisation and concerns regarding social justice are intertwined in contemporary higher education systems.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".