Forecasting University Funding: A Non-Linear Approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Are enrollment-based funding formulas really dependent on enrollment? The recent changes in funding for universities in the province of Quebec, Canada, suggests a disconnect between subsidies and enrollment despite the funding being enrollment based. This disconnection is observed when using a linear model to forecast the funding of the different universities in Quebec. The results show that simply considering linear mechanisms in the models consistently underestimates the funding. This paper explores the importance of taking into consideration these non-linear mechanisms in the funding formula. We estimate the funding models with non-linear vector autoregressive and the margins of error with bootstrap methods. This allows us to directly estimate the funding formula, and thus the non-linear components. We find that the non-linearities are important to explain the funding trends. In particular, the smoothing mechanism, the increase in funding per student and other exceptions leads subsidies to increase despite a stagnation or a decline in enrollment. Moreover, the model developed in this article also provides a ready-made recipe for forecasts in other jurisdictions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it