Incentive-Based Budgeting Systems in Public Universities
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
Why incentive-based budgeting systems in the public sector and why now?, Douglas M. Priest, William E. Becker, Don Hossler, Edward P. St John. Overview: the case, if any, for responsibility centre budgeting, Edward L. Whalen the efficiency of responsibility centre management within state universities, John Douglas Wilson revenue flux and university behaviour, Larry L. Leslie, Ronald L. Oaxaca, Gary Rhoades. Case studies: responsibility centre budgeting and management at Indiana University, Kenneth R.R. Gros Louis, Maynard Thompson responsibility centre budgeting at the University of Toronto, Daniel W. Lang activity-based budgeting at the University of Michigan, Paul N. Courant, Marilyn Knepp. Effects and lessons: budget incentive structures and the improvement of college teaching, Michael B. Paulsen, Edward P. St John reward structures and faculty behaviour under responsibility centre management, William E. Becker, Neil D. Theobald using performance indicators to evaluate decentralized budgeting systems and institutional performance, Robert K. Toutkoushian, Cherry Danielson incentive-based budgeting - an evolving approach, Douglas M. Priest, Edward P. St John, William Tobin.
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.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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