Key Points in Implementation of Knowledge Management and its Solutions
Bibliographic record
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; 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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.017 | 0.011 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 0.006 |
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".