Towards a Knowledge-Based Framework for Enterprise Content Management
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Nowadays, critical information that is contained in mostly unstructured documents is increasingly becoming a key business resource. Accordingly, enterprises need a foundation for managing content to understand its value and transform it into information and organizational knowledge. Enterprise Content Management (ECM) is an integrated approach to Information Management. There is a need for enhancing this approach to support the transformation from information into organizational knowledge. However, assessing, organizing, sharing, and using content based on knowledge perspectives are crucial, especially for knowledge-intensive enterprises. Those enterprises provide knowledge-intensive products and services that require a robust foundation for knowledge management and innovation capacity. We present the KBCM (Knowledge-Based Content Management) framework for ECM based on the perspective of knowledge components. This paper seeks to create more business value by transforming content into valuable information assets and then from information into organizational knowledge. To demonstrate the framework, an illustrative example is constructed and evaluated.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it