Application of the CAMPUS Simulation Models to the Major Planning Decisions of a Large University
Bibliographic record
Abstract
Administrative planning and budgeting procedures within universities usually center around an argumentative pie-cutting process. Various factions argue their case for incremental increases in their budgets by moving from general statements of objectives to very specific requests for additional resources. Lack of a formal link between these two extremes makes it virtually impossible for senior administrative bodies to assay the justification of the request. An exploration and structuring of this middle ground between generalized objectives and specific resource requests must be undertaken if colleges and universities are to meet the mounting pressures on them to use their resources wisely. Educators will have to be more systematic in deciding on the physical and financial needs of new or expanded institutions, justifying budget requests to governments, foundations, etc. and allocating funds to competing users within the institution.CAMPUS (Comprehensive Analytical Methods for Planning in University Systems) is an attempt to close this gap. CAMPUS, under development since 1964 at the University of Toronto, is composed of three integrated components.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.002 | 0.002 |
| 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 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".