A Framework to Clarify the Role of Knowledge Management Systems.
Bibliographic record
Abstract
Knowledge management Systems (KMS) are IT applications that manage representations of organizational knowledge. This paper presents a conceptual model of KMS which adapts an artificial intelligence (AI) based view of knowledge. According to this view, knowledge can be defined in terms of agent, action, state, and goal. The conceptual model is intended to help differentiate the role of KMS from that of Information Systems (IS). The knowledge managed by the KMS is intended to enable an agent to choose actions that can be taken to accomplish a goal in the given state. The role of the IS is to make the agent aware of a situation described in terms of states, actions, and goal. The model suggests that whether we classify an IT application as KMS or IS depends on the contents it manages and to increase the effectiveness of KMS, it is often necessary to use IS that complement the KMS. Considering the importance and popularity of KMS in organizations, we believe the clarification of the role of KMS is useful.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.005 | 0.023 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 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".