Challenges and Drawbacks in the Marketisation of Higher Education Within Neoliberalism
Bibliographic record
Abstract
This paper addresses some of the challenges and drawbacks associated to the ongoing worldwide process of marketization (neoliberalization) in higher education. Neoliberalism—the prevailing model of capitalist thinking based on the Washington Consensus—has conveyed the idea that a new educational and university model must emerge in order to meet the demands of a global productive system that is radically different from that of just a few decades ago. The overall argument put forward is that the requirements, particularly the managerial and labor force needs of a new economy—already developing within the parameters of globalization and the impact of information and communication technologies (ICTs)—cannot be adequately satisfied under the approaches and methods used by a traditional university. Neoliberalism affects the telos of higher education by redefining the very meaning of higher education. It dislocates education by commodifying its intrinsic value and emphasizing directly transferable skills and competencies. Nonmonetary values are marginalized and, with them, the nonmonetary ethos that is essential in sustaining a healthy democratic society. In this paper I will address (1) some of the problems and shortcomings in the triple-helix model of university-industry-government collaborations, (2) the transformation of students into customers and faculty into entrepreneurial workers, highlighting the many drawbacks of such strategies, (3) the hegemony of rankings as procedures of surveillance and control, (4) the many criticisms posed against neoliberalization in higher education and the possible alternatives looking to the future.
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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 | 0.053 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.061 |
| Scholarly communication | 0.024 | 0.028 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 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".