Enterprise Content Management Systems as a Knowledge Infrastructure
Bibliographic record
Abstract
The rise of the knowledge-based economy has significantly transformed the economies of developed countries from managed economies into entrepreneurial economies, which deal with knowledge as both input and output. Consequently, knowledge has become a key asset for organizations and knowledge management is one of the driving forces of business success. One of the most important challenges faced by enterprises today is to manage both knowledge assets and the e-collaboration process between knowledge workers. Critical business knowledge and information is often contained in mostly unstructured documents in content management systems. Therefore, content management based on knowledge perspectives is crucial for organizations, especially knowledge-intensive organizations. Enterprise Content Management has evolved as an integrated approach to managing documents and content on an enterprise-wide scale. This approach must be enhanced in order to build a robust foundation to support knowledge development and the collaboration process. This paper presents the KBCM (Knowledge-Based Content Management) framework for constructing a knowledge infrastructure based on the perspective of knowledge components that could help enterprises create more business value by classifying content formally and enabling its transformation into valuable knowledge assets.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".