A New Equation for Human Intellectual Capital Management Intelligent Website for Managing Communities of Practice of Academic Organizations and Financial Investments by Using Adaptive and Dynamic Assessment Networks
Bibliographic record
Abstract
In the forthcoming twenty century, economic system of each country should be driven based on sciences, technology and innovations developed by specific knowledge from specialists called knowledge workers. This is an important strategy of Thailand to develop new products and services for surviving in serious business competition. Therefore, essential knowledge in knowledge workers such as skills and experiences for developing new products, services and innovations are required. This tacit knowledge (knowledge in human) should be appropriately measured and managed in the perspective of intellectual capital or intangible capital management. This paper attempts to answer the question “How to appropriately measure tacit knowledge by using graph theory?”. Thus, a new equation to measure intellectual capital for managing excellent centers and laboratories is presented in this paper. This is the novel impact factor equation which considers the dimension of time and frequency of published articles combined with the other impact factors of each published paper on Scimago Journal Rank website and Scopus. Moreover, it is not only the formula which is the quantitative intellectual capital assessment but the intellectual capital indicators and the intellectual capital analysis for university are illustrated in the form of qualitative intellectual capital assessment also. Moreover, intellectual capital management intelligent website for managing Communities of Practice (CoP) of academic organizations and financial investments by using new equation and Dynamic Assessment Networks (DANs) is developed also. The proposed concept and methodology is a basis for risk management in terms of human capitals and financial investments.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".