Collaborative knowledge networks emergence for innovation: Factors of success analysis and comparison
Bibliographic record
Abstract
New product development needs new engineering approaches. Knowledge is a key resource that impacts traditional, organisational, economic and innovative models. Through NICT (New Information and Communication Technologies), globalisation encourages the emergence of networks that overcome traditional organisation boundaries. International enterprises, European-Community Networks of Excellence or Clusters (competitiveness poles) indicate the need to define a new way of thinking. This new way moves towards an agile, continuous innovative use of knowledge. Based on an epistemic study of knowledge management best practices, four examples show the barriers that can be encountered today. This paper aims defining the key elements that enhance collaborative networks. The analysis of best practices from collaborative environments enables the design of high standard information systems and initiate knowledge ecosystems. A balance between formalism required to share knowledge and fuzziness of social networks triggers new initiatives. This ensures the validity of information exchange through virtual collaboration. It helps to maintain group coherence despite exceeding the natural maximum number of collaborators. Finally the main success or failure factors are highlights and commented to ease the transition from economic-driven to expertise-driven models is then facilitated.
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.
How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".