Bibliographic record
Abstract
Abstract The aim of this article is to analyse appropriateness and adequacy of use of Data Envelopment Analysis (DEA) in several research papers dealing with effectiveness of economy of universities. The Data Envelopment Analysis is an interesting method used for evaluation of technical efficiency of production units. Comparison is the basic method of this article. At the beginning, basic methodological questions of measurement and evaluation of efficiency are analysed, including definitions of terms efficiency and effectiveness, ways of measurement and formulation of appropriate indicators. Based on the given perquisites for measurement and evaluation of efficiency five articles on evaluation of efficiency of universities using DEA method, published in Canada, Australia, Great Britain, Germany and Spain in 1998 - 2008, will be assessed. DEA is able to use more parameters of input and output to evaluate which of units under examination is the most effective, and to compare other units with it. For this, it is necessary to have a homogenous group of units. The result of assessment shows that all the examined studies focused rather on way of calculation then the point and reason of measurement. The articles contain a discussion concerning choice of appropriate indicators but do not at all deal with the issue of its construction using interventional logic; the articles do not contain any comparison of objectives of the particular universities. Evaluation of efficiency of universities is a social construct and it will always be a subjective matter related to objectives of a particular stakeholder. This fact explains how to approach the evaluation of efficiency: it is necessary to set an objective function that means to set the objectives of a given stakeholder and his preferred results and outputs. All the studies lack this basic logic.
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 | 0.013 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".