Evaluating the Dynamics of Knowledge-Based Network Through \nSimulation: The Case of Canadian Nanotechnology Industry
Bibliographic record
Abstract
Collaboration is a major factor in the knowledge and innovation creation in emerging science-driven industries, where the technology is rapidly changing and constantly evolving, such as nanotechnology. The scientific collaborations among individuals and organizations form knowledge co-creation network within which information is shared, innovative ideas are exchanged and new knowledge is generated. Although various simulation attempts have been carried out recently to analyze the performance of such networks at the firm level, the individual level has not been much explored in the literature yet. \nThe objective of this thesis is to investigate the role of individual scientists and their collaborations in enhancing the knowledge flows, and consequently the scientific production within the Canadian nanotechnology scientists. The methodology involves two main phases. First, in order to understand the collaborative behavior of scientists in the real world, the data on all the nanotechnology journal publications in Canada was extracted from the SCOPUS database and the scientists' research performance and partnership history was analyzed using social network analysis. Moreover, the predominant properties that make a scientist sufficiently attractive to be selected as a research partner were determined using data mining and through a questionnaire sent directly to the researchers selected from our database. In the second phase, an agent-based model using Netlogo has been developed to simulate the knowledge-based network where several factors regarding the ratio, existence and absence of various categories of scientists could be controlled. \nIt was found that scientists in centralized positions in such network have a considerable positive impact on the knowledge flows, while loyalty and cliquishness negatively affected the knowledge transmission. Star scientists appear to play a substitutive role in the network as most famous and trustable partners to be selected when usual collaborators are scarce or missing. Besides, the changes in the performance of some categories in case of the absence of others have been also observed. \nThe major contribution of this work stems from the fact that the developed simulation model is the first one, which is fully based on the real data and on the observed behavior of the scientists in knowledge-based network.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".