Principles Based Budgeting: Resources for Revisioning Academic Planning
Bibliographic record
Abstract
In working toward a budgeting framework that responds to the often harmful impacts of neoliberal accounting practices on people and places, this research has been guided by deep-rooted principles that were gifted to the University of Saskatchewan, through a rigorous Indigenous-led community consultation process which interpreted institutional strategic principles, using Cree and Michif terms: nākatēyihtamowin | nakaatayihtaamoowin (sustainability), nihtāwihcikēwin | nihtaooshchikaywin (creativity), nanātohk pimātisowina | nanaatoohk pimatishoowin (diversity), and āniskōmohcikēwin | Naashkoopitamihk (connectivity). This consultation demonstrated the pressing need to redefine what a successful budgeting framework might mean by looking beyond the role of a financial plan and adopting a more broad-based approach using socially and environmentally responsible lenses that incorporate new directions based on Indigenous knowledges, world views, and values invested in creating a more inclusive and productive campus in targeted, incremental, and structural ways. This exploratory study builds on information gathered internally from the university’s student governance structures, broad conversations within an ad hoc advisory group, and relevant literature. An important role of budgeting is that it can guide performance measurement and management; our exploration included looking for ways to identify potentially “new-old” measurements of success as they pertain to the university’s stated objectives and aspirational goals. Current challenges of resource allocation faced by the university were reviewed to identify bottlenecks based on funding limitations that cause barriers to accessibility to academic and non-academic supports, and undesirable environmental effects. Our study raises more questions than answers, but provides insight into potential future processes, which we anticipate in this field report.
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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 | 0.078 | 0.161 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.029 | 0.034 |
| Open science | 0.007 | 0.024 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.053 | 0.015 |
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".