Towards the reconciliation of knowledge management and e‐collaboration systems
Bibliographic record
Abstract
Purpose The purpose of this paper is to propose an intelligent infrastructure for the reconciliation of knowledge management and e‐collaboration systems. Design/methodology/approach Literature on e‐collaboration, information management, knowledge management, learning process, and intellectual capital is mobilised in order to build the conceptual framework. Findings This paper presents a conceptual framework including a set of concepts and guidelines that can be used to specify an efficient knowledge infrastructure for networked enterprises. Research limitations/implications Results from this study uphold the emerging research area of knowledge management in e‐collaboration systems. The proposed framework derived purely from theory and conceptual analysis; more work needs to be done in order to validate and experiment with the framework. Future research remains be carried out to apply the framework on a broader scale, and in particular to determine its applicability relative to various collaboration patterns and current technology development. Practical implications Results from this study are important for networked enterprises, especially knowledge‐intensive enterprises, who intend to build e‐collaboration systems to organize their knowledge base and to share it with their partners. Originality/value This paper is one of the first to address collaborative knowledge management in e‐collaboration systems with a focus on the promotion of learning process and the creation of intellectual capital.
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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 | 0.030 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.019 | 0.038 |
| Open science | 0.005 | 0.021 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".