A note to ‘Enterprise risk management: a DEA VaR approach in vendor selection’: a response to Wei and Wang and model extension
Bibliographic record
Abstract
Enterprise risk management (ERM) has become an important topic in today's more complex, interrelated global business environment, replete with threats from natural, political, economic and technical sources. Wu and Olson (Citation2010) [Wu, D.S. and Olson, D., 2010. Enterprise risk management: a DEA VaR approach in vendor selection. International Journal of Production Research 48 (16), 4919–4932] present a state-of-the-art overview of Enterprise risk management and discuss the possibility of constructing a value at risk measure at the data envelopment analysis (DEA) framework. Wei and Wang [Wei, G.W. and Wang, J.M., 2011. Value-at-risk and data envelopment analysis: comments on Wu and Olson (Citation2010). International Journal of Production Research, 49 (23), 7189–7193] contend errors in the article. Wei and Wang suggest a model based on Li [Li, S.X., 1998. Stochastic models and variable returns to scales in data envelopment analysis. European Journal of Operational Research, 104, 532–548]. We show that the suggested model in Wei and Wang (Citation2011) does not solve the problem completely. We provide alternative approaches to conduct performance evaluation with good discriminating power.
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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 | 0.030 | 0.089 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.010 | 0.025 |
| 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".