KNOWLEDGE FOR COMPETITIVE ADVANTAGE: PUTTING KNOWLEDGE AT THE CORE OF THE BUSINESS By
Bibliographic record
Abstract
In recent years, the words- knowledge and managing knowledge are at the core of management thinking. Scholars and business strategists are trying to explore the real nature of knowledge and its management from the organiza-________________________ 1Mr. Abu Saleh Md. Sohel-Uz-Zaman obtained his Ph.D. degree from the Wuhan Uni-versity of Technology, P R China, where his specialization was knowledge innovation. Mr. Zaman also has an MBA from Middlesex University, London, UK with a major in General Management. He received Certified Financial Consultant (CFC) certification from the Institute of Financial Consultant (IFC), Canada. Presently Mr. Zaman is working as an Assistant Profes-sor in the School of Business, United International University, Dhaka, Bangladesh. His areas of interest are human resource management, strategic management and knowledge manage-ment. He has a good number of publications in international and local journals, conferences and forums. 2Ms. Umana Anjalin holds an MBA degree from the Institute of Business Administra-
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.030 |
| Scholarly communication | 0.028 | 0.028 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.008 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".