Multi-period efficiency evaluation of heterogeneous decision-making units: Assessing efficiency before, during, and after COVID-19
Bibliographic record
Abstract
Data Envelopment Analysis (DEA) is an effective method for evaluating and improving the performance of Decision-Making Units (DMUs). It utilizes mathematical programming models to compare homogeneous units based on their inputs and outputs. One of the major challenges in this field is assessing the efficiency of DMUs over different time periods, where heterogeneity, arises due to various factors, such as, scientific and technological advancements, political and economic changes, system management updates, etc. These changes may lead to the addition or elimination of outputs, making unit comparisons more complex and creating significant differences in efficiency. Traditional DEA methods often fail to account for these changes across time periods simultaneously. Therefore, there is a need for new approaches, as to assess the efficiency of DMUs under such conditions. This paper addresses these challenges and presents a novel approach for analyzing the efficiency of DMUs undergoing substantial changes over time. As a practical application, the results of an empirical study evaluating conferences throughout three time periods, namely, (before the COVID-19 outbreak, during the pandemic, and after the pandemic) have been presented. These findings demonstrate the efficiency of the proposed approach effectively and can significantly assist organizations in more accurate evaluations and the enhancement of performance, relative to the evolving units.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.031 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.019 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".