A Comparison of Quality Awards Program in the Major G-20 for Developing a Korean National Quality Award Model
Why this work is in the frame
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Bibliographic record
Abstract
【To implement total quality management(TQM), firms and institutions have strategically used quality awards models. In this paper we analyzed the national quality awards of G-20 members such as United States, Europe, Japan, Canada, Australia and Korea. There are three main points to analysis; First, type of model is good for Korea?, even though Korea already has adopted MBNQA model. Second, Are the core values really different from each models?. And third, Is there any difference in the criteria structure and value points system? This study aims to design a National Quality Award which is good for the Korean companies and organizations. After analyzing the current quality awards models, we propose some suggestion about core value setting Korean-specific criteria development and value points system change.】
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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 | 0.014 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it