Quantifying administrative efficiency: proposed figures of merit for university comparisons and ranking
Bibliographic record
Abstract
Administrative costs at universities have been steadily increasing in U.S. schools. These overheads are not currently directly captured in university ranking systems. In this study two approaches to administrative efficiency are proposed to overcome this oversight by measuring the ratio of administrators to faculty members and the ratio of students to administrators. Both values can be acquired from free publicly available databases and calculated easily to determine which universities are preferentially investing more in administration rather than directly into the education of their students. In this study, these approaches are compared, and open source software is provided for others to automate this basic calculation. The software was tested on data from 436 schools in the data set from the U.S. News and World Report on National Universities. The results show that only a few universities have less administrators and staff than faculty members. Remarkably, many universities have more administrators than students, and it appears clear that tuition increases are largely due to administrative bloat. The use of both methods in university ranking calculations would provide incentives to increase administrative efficiency and make university education more accessible to less-affluent students thereby encouraging meritocracy and concomitant economic development.
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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 | 0.068 | 0.202 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.033 | 0.025 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".