The “continuumization” of knowledge management technology
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
Purpose This paper aims to explain why a different technology for knowledge management (KM) is needed. It also investigates the new trends in knowledge management technology (KMT), and shows how the new technology can be aligned with KM principles to satisfy business goals. Design/methodology/approach This paper interprets array of literature in the area of KMT as related to its importance and development. It provides a roadmap to how technology may ascend to the level of the KM cognitive process. This can only be achieved, if KMT presents itself as an authentic conduit for knowledge, and not only a channel for the lower end of the continuum. Findings So far, KMT is not mature enough to deliver bona fide KM processes. The distance from data to knowledge cannot be handled by the existing technology unless technology cast off its bivalent logic. Despite the recent leaps in technology in general, the situation is still perplexing and elusive. This is because KMT deals with the knowledge continuum sets either as discrete unrelated events or as one class with no different technological requirements. Practical implications KMT has become increasingly complicated and confusing. This paper will explain why KMT has not fulfilled its promise yet, and how this fact can be used to avoid technology selection pitfalls. Originality/value The paper provides a roadmap for KM practitioners for evaluating KMT functionalities as related to the type of knowledge needed in their organizations for achieving competitive advantage.
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 | 0.001 |
| 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