The 'Mobilization-Network' Approach for the Social Network Analysis of Knowledge Mobilization in Science Research and Innovation
Bibliographic record
Abstract
The main goal in this paper is to establish a theoretical and empirical basis for the social network analysis of knowledge mobilization in science research and innovation: the mobilization-network approach. The approach captures knowledge mobilization within and beyond academia. A starting point is the identification of knowledge gaps in the investigation of knowledge mobilization, namely in bibliometric studies measuring impact mostly within academia and in name generator techniques relying solely on individuals’ recall of network ties. In contrast, networks built using a mobilization-network approach make more visible the relations among heterogeneous academic and non-academic actors. These include individual and organisational actors (i.e., researchers, students, policy-makers, funders, laboratory products, and civil-society groups) and mobilization actors (i.e., laboratories, publications, research projects, policies, media events, and business ventures). The longitudinal empirical case study of a basic science laboratory illustrates the approach. Finally, the mobilization-network approach can be an asset for policy-makers wishing to evaluate the impact of science and innovation, especially where knowledge mobilization related policies are in place.
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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 | 0.184 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.049 | 0.505 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.004 | 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; both teacher heads agree on what is shown here.
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".