Towards the reconciliation of knowledge management and e‐collaboration systems
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 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.
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 | 0.000 |
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