Government Oversight of Organizations Engaged in Multiple Activities: Does Centralized Governance Encourage Quantity or Quality?
Bibliographic record
Abstract
In situations where organizations perform multiple tasks, is it better for government oversight to be centralized or decentralized? In the private sector, strong incentives are used to induce effort: sometimes in the form of bonuses, stock options, piece-rate schemes, and commission sales. Strong incentives, however, are frequently unable to achieve efficient outcomes. If one is operating in an environment whereby multiple tasks are performed but are not all easily observed, a strong incentive scheme will fail to produce an efficient outcome. Under this type of scenario, previous research has suggested that slack incentives should be used. Although much of the literature on multitasking has focused on private sector incentives, similar problems exist in the public sector. In the public sector, different tools are available to address this problem. A public sector organization generally has limited resources and is often not able to use performance based rewards. In the public sector, it has become popular to develop a set of performance measures to assess a governmental unit’s productivity. We analyze how centralization of oversight and funding affect research activities within a public institution for a set of public universities, focusing on quantity and quality measures. Our results suggest that quality is higher at institutions within a centralized framework. Moreover, these results hold only for those institutions that are not considered to be the state's flagship university. To explain why centralization may be better in this instance, we explored several alternative hypotheses. We find the evidence supports the hypothesis that centralization improves performance insofar as it encourages potential externalities to be internalized when several public organizations are forced to compete and cooperate with each other.
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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 | 0.018 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".