Equity and purpose in financing universities: the case of Nova Scotia
Bibliographic record
Abstract
Abstract: Nova Scotia has struggled with the funding of its universities ever since it introduced provincial grants in 1963. This is because some of its rather large number of universities and colleges are in very different situations as compared with others. How is an equitable distribution to be achieved under such circumstances? The longstanding solution has been to combine fixed grants based on a historically determined base allocation, with a variable component based on enrolment. But this grew increasingly problematic as the original institutional patterns evolved and, especially, when the province adopted a formula of fixed shares combined with actual reductions in funding. A new formula was required and this article tells the story of its development by the Nova Scotia Council on Higher Education. This article explores both the issues that had to be addressed and resolved and the institutional dynamics and personalities that shaped their resolution. In the end, it became apparent that an equitable redistribution of grants to Nova Scotia's universities could be achieved through this process only if additional resources were forthcoming. Sommaire: La Nouvelle‐Écosse se débat avec le problème du financement de ses uni‐versités depuis l'adoption des subventions provinciales en 1963, éant donné la situation très particuliére de plusieurs de ses nombreux colléges et universités. Comment réaliser une répartition équitable dans de telles circonstances? Pendant longtemps, la solution fut de combiner des subventions fixes s'appuyant sur une allocation de base établie de longue date, avec une composante variable fondée sur le nombre d'inscrip‐tions. Mais, cette méthode est devenue de plus en plus problématique face à l'évolu‐tion des modèles institutionnels et, en particulier, lorsque la province a adopté une formule de parts fixes combinée à des coupures de financement. II a fallu alors trou‐ver une nouvelle formule, et cet article présente l'historique de son élaboration par le Conseil de l'enseignement supkérieur de la Nouvelle‐Écosse. II explore à la fois les questions qu'il fallait régler et la dynamique institutionnelle ainsi que les personna‐lités qui ont influé sur leur résolution. En dernière analyse, il devint évident qu'une redistribution équitable des subventions aux universités de la Nouvelle‐Écosse ne pouvait se réaliser par l'entremise de ce processus qu'avec l'allocation de ressources supplémentaires.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".