How to Evaluate the Performance of the Taiwan Biotech and Biopharmaceutical Corporations?
Bibliographic record
Abstract
Modern biotechnology and pharmaceutical industry, the most developmental mainstream, has been generally acknowledged in the 21st century. The approach of this study surmounted the traditional DEA and SFA, combining with modified Delphi approach, ISM, FANP and performance evaluation table to build evaluation mode of operating performance precisely and completely in Taiwan biotech and pharmaceutical corporations. Considering 4 criteria and 18 sub-criteria complied to evaluate the operating performance in the enterprise. The analyzed result appeared, the significance of criteria is “Product and technology R&D”, “Financial performance”, “Production and quality management” and “Organization characteristics and operation management” in sequence. The top 5 key sub-criteria influence the evaluation of operating performance in Taiwan biotech and pharmaceutical corporations are “Profitability”, “Efficiency of production and cost”, “Innovative products and R&D strategies”, “Quality management and cost control” and “Operation strategy and business mode”. The last 5 key sub-criteria are “Human resources management”, “Project management”, “Innovation of process technology”, “Competence of financial operation” and “Market share”. Finally, the top 10 of conglomerate revenue in listed companies taken as the sample of empirical research on this study. According to the experts’ evaluation, the total point of weighted average is 58.9020 of whole sample in complete period in all enterprises, which fell at the grade of ‘Slightly good’ as a whole. The related results accord with the real situation in the industry. The result of this study is able to be a significant basis as the policies drawn up by government, operating performance evaluated by the enterprise and investment target measured by the investors.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 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; a candidate call from one teacher head, 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".