Analyzing Neoliberalism in Theory and Practice: The Case of Performance-Based Funding for Higher Education
Bibliographic record
Abstract
Neoliberal ideas – whether the new public management, principal-agent theory, or performance management – have provided rationale for sweeping reforms in the governance and operation of higher education. Despite this, little attention has been devoted to how well neoliberal theory illuminates the policy process by which neoliberal policy is enacted and implemented. This paper expands our understanding of the origins, implementation, and impacts of neoliberal policies by examining the case of performance-based funding (PBF) for higher education in the United States, Europe, Canada, Australia, and elsewhere. With regard to policy origins, neoliberal theory anticipates the key role that top government officials play in the development of PBF but fails to anticipate the important roles of business and higher education institutions in the formation of neoliberal policies. Neoliberal theory notes the important role of monetary incentives as policy instruments and the obstacles posed by gaming on the part of agents, but the implementation of PBF also involves other policy instruments and faces additional obstacles to implementation. Policy outcomes fitting the neoliberal focus on organizational effectiveness and efficiency are only weakly produced by PBF, but PBF is associated with a host of unintended impacts that neoliberal theory ignores.
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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 | 0.039 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.060 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.007 | 0.007 |
| 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".