Key Points in Implementation of Knowledge Management and Its Solutions/POINTS CLÉS DANS LA MISE EN OEUVRE DE LA GESTION DES CONNAISSANCES ET DE SES SOLUTIONS
Bibliographic record
Abstract
Abstract: In knowledge-base economy era, knowledge becomes the most important resource for enterprise instead of physical labor, capital and natural resources. The success of the enterprise depends more and more on the quantity and quality of knowledge owned by it. The core competitiveness originates from employees' innovation ability which comes from knowledge accumulation and knowledge management. How to manage the knowledge possessed by the enterprise and how to make it becoming the sustaining competitive advantages for the enterprise? This is a new problem we must face. Finding the key points of knowledge management and plan the solution path are the crux to settle this problem. Key words: Knowledge; Management; Innovation; Competitiveness Resume: Dans l' ere de l'economie de la connaissance, la connaissance devient la ressource la plus importante pour les entreprises au lieu de travail physique, des capitaux et des ressources naturelles. Le succes de l'entreprise depend de plus en plus de la quantite et de la qualite de la connaissance detenue par elle meme. Les competitivites principales viennent de la capacite d'innovation des employes qui provient de l'accumulation des connaissances et la gestion des connaissances. Comment faire pour gerer les connaissances possedees par l'entreprise et comment faire pour qu'elles deviennent des avantages concurrentiels soutenus pour l'entreprise? Il s'agit d'un nouveau probleme a qui nous devons faire face. Pour regler ce probleme, il faut trouver les points cles de la gestion des connaissances et planifier les moyens de solution. Mots-Cles: connaissances; gestion des connaissances; innovation; competitivites 1. KNOWLEDGE AND KNOWLEDGE MANAGEMENT is the cognition sum owned by enterprise or individual which is accumulated through long-term learning and practice. It contains explicit knowledge and tacit knowledge. Explicit knowledge can be expressed by language, character, data, figure, picture, video or knowledge products containing patent and software. While tacit knowledge can not be seen or heard by people. It includes experience, skill, know-how, personal insight, intuition and premonition. The tacit knowledge is attained through practice and preserving in human's brain, personal ideal and value. Francis Bacon, a British philosopher once to said: Knowledge is power. This logion is applicable both to enterprise and individual. management (hereafter called KM) is the integration procedure of data collection, classification, analysis, sharing within the organization. The procedure combines the Management Information System with the learning experience. It uses the collective wisdom to improve the entire ability of innovation and managing the changes. Through open structure, enterprise can collect, process and share enough knowledge to upgrade employees' creativity and make enterprise grow. The essence of KM is to fully explore, accumulate and use enterprise's knowledge resources (including explicit knowledge and tacit knowledge) and transform them into corporate competitiveness. The aim of KM is knowledge innovation which is the interactional result of different knowledge procedure containing knowledge production, knowledge sharing, knowledge application and knowledge innovation. In an enterprise where knowledge was not managed, the general form of knowledge is tacit. It disperses in pieces in the enterprise and the knowledge innovation is an occasional individual behavior. The aim of KM is to change this situation. It will make the tacit knowledge explicating, structurizing, popularizing and finally make the knowledge innovation procedure standardizing. KM, since it was introduced in management area in the middle of nineties of the 20th century has been becoming an important branch of management research and a useful tool in practice to entirely improve the performance of the enterprise. …
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".