Responsibility Center Budgeting as a Mechanism to Deal with Academic Moral Hazard
Bibliographic record
Abstract
Universities face inherent informational asymmetries. These make university budgeting prone to various challenges including moral hazard. The last forty years has seen some large research- intensive universities move from centralized incremental budgeting to decentralized Responsibility Center Budgeting (RCB). It is assumed that a faculty chooses a level of costly effort in generating revenue for the university. The level of faculty effort is not observable by the central administration. When there is no revenue uncertainty or when the faculty is not risk averse, pure RCB is best from the perspective of the administration. The intuition is that pure RCB fully aligns financial responsibility with academic authority, that is, it makes the faculty the residual claimant. Once the faculty is risk averse, partial RCB is optimal. Partial RCB provides a balance between providing the right incentives to the faculty and the university reducing the revenue risk faced by the faculty.
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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 | 0.019 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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 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".