A composite index for measuring performance in higher education institutions
Bibliographic record
Abstract
Purpose – Governments and funders are increasingly linking the funding of higher education institutions (HEIs) to their performance. Performance indicators (PIs) provide a means to measure and track performance of HEIs. The purpose of this paper is to provide a structured framework for mapping out key PIs and developing a composite index for measuring performance in HEIs. Design/methodology/approach – The paper makes use of the analytic hierarchy process to develop the framework. The application of the framework is demonstrated through a case study. Findings – A structured approach to determining key PIs and developing a composite index in HEIs is elaborated. The framework developed in this paper is consensus-based, knowledge-intensive, and allows input to and ownership of the decision process and its output. Practical implications – While there are numerous PIs; organizational resources and capabilities to manage these PIs are usually limited. HEIs must manage and improve their performance within their unique contexts. This paper provides a methodology to do so. Originality/value – The process of mapping out key PIs and developing composite indices for integrated performance measurement are not adequately understood and need further research. The framework discussed in this paper has not been elaborated on in previous publications.
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 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 | 0.017 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.020 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".