Research of Quality Improvement and Quality Innovation Based on Knowledge Fermenting Model
Bibliographic record
Abstract
Quality improvement and quality innovation are the important approach to enhance competitive force for enterprises. Quality improvement is a process of knowledge innovation in nature which must be actualized by organizational learning.This article starts from the relations among quality improvement, quality innovation and the ability of organizational learning, analyzes the knowledge moving rule in the interior process of quality improvement and innovation and expatiates on the creation, development and diffusion mechanisms of quality knowledge in the quality flow based on the knowledgefermenting theory of organizational learning. Furthermore, taking the quality control (QC) group as an example, this article analyzes the behaviors and functions of nuclear factors such as quality knowledge sourdough, quality knowledge matrix and quality knowledge enzyme in the process of quality knowledge fermenting, and these nuclear factors function mutually in the quality knowledge fermenting bar. This article also puts forward five types of quality knowledge fermentation and analyzes their characters respectively, points out the implementation of knowledge fermentation possesses meanings to enhance the level of quality improvement and quality innovation for enterprises.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".