Fiscal incentives, Clark’s triangle, and the shape and shaping of higher education systems
Bibliographic record
Abstract
For nearly 35 year’s Burton Clark’s triangle has been used as a paradigm for describing, assessing, and comparing systems of post-secondary education. Since then two major developments, neither of which could Clark have foreseen, in the financial management of higher education have occurred contemporaneously: incentive or performance funding on the part of the state and incentive-based budgeting on the part of universities. Both developments are based on fiscal incentives. Despite several inherent and inter-connected similarities, incentive funding and incentive-based budgeting have been appraised on parallel tracks, neither of which has led to a possible effect on Clark’s fundamental model, particularly with regard to the interaction of institutional behavior as it is shaped by and shapes systems of higher education. This study investigates their convergence with one another and the consequential effect on the relationship between the state, the university, and the market as foreseen by Clark’s Triangle. The study concludes that, although incentive funding and incentive-based budgeting are sometimes at cross-purposes, they are functionally so inter-connected, whether intentionally or coincidentally, and that they may change the shape of a given system’s 'triangle' by altering the zero-sum balance between the state, market and academic legs of the triangle.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.032 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".