Empirical Study of Integrated EVA Performance Measurement in China
Bibliographic record
Abstract
Traditional performance measurement has some limitations. Economic Value Added (EVA) is a real method to measure the company’s true value. This paper discussed on how to improve traditional performance measurement with EVA. It presented the integrated EVA performance measurement (IEPM) model. The superiority of IEPM model to traditional performance measurement was empirically analyzed with BP neural network and the data from China’s listed companies. The results showed that the measurement ability of IEPM model was superior to that of traditional performance measurement. Its prediction ability was also proved to be better than that of traditional measurement. It suggests that introducing EVA to performance measurement well reflects the company’s real profit. So it is effective and reasonable to use IEPM model to evaluate and predict the company’s performance. Key words: Economic value added, IEPM model, Neural network, Performance measurement Resume: Traditionnels de mesure du rendement a quelques limitations. Economic Value Added (EVA) est une vraie methode pour mesurer la vraie valeur de l'entreprise. Ce document discute sur la maniere d'ameliorer la mesure du rendement traditionnel avec EVA. Il a presente les mesures de la performance integree EVA (IEPM) modele. La superiorite de l'IEPM modele traditionnel de la mesure du rendement a ete empiriquement analysees avec BP de reseaux de neurones et les donnees provenant de la Chine societes cotees. Les resultats ont montre que la mesure de la capacite IEPM modele a ete superieure a celle de la traditionnelle mesure de la performance. Sa capacite de prevision a egalement ete revelee meilleure que celle de la mesure traditionnelle. Il suggere que l'introduction de l'EVA a la mesure du rendement reflete bien la societe profit immobilier. Ainsi, il est efficace et raisonnable d'utiliser IEPM modele pour evaluer et prevoir les resultats de la societe. Mots-Cles: Economic value added, IEPM modele, Neural network, Mesure de la performance
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".