Using Network Analysis to Understand Knowledge Mobilization in a Community-based Organization
Bibliographic record
Abstract
BACKGROUND: Knowledge mobilization (KM) has been described as putting research in the hands of research users. Network analysis is an empirical approach that has potential for examining the complex process of knowledge mobilization within community-based organizations (CBOs). Yet, conducting a network analysis in a CBO presents challenges. PURPOSE: The purpose of this paper is to demonstrate the value and feasibility of using network analysis as a method for understanding knowledge mobilization within a CBO by (1) presenting challenges and solutions to conducting a network analysis in a CBO, (2) examining the feasibility of our methodology, and (3) demonstrating the utility of this methodology through an example of a network analysis conducted in a CBO engaging in knowledge mobilization activities. METHOD: The final method used by the partnership team to conduct our network analysis of a CBO is described. RESULTS: An example of network analysis results of a CBO engaging in knowledge mobilization is presented. In total, 81 participants completed the network survey. All of the feasibility benchmarks set by the CBO were met. Results of the network analysis are highlighted and discussed as a means of identifying (1) prominent and influential individuals in the knowledge mobilization process and (2) areas for improvement in future knowledge mobilization initiatives. CONCLUSION: Findings demonstrate that network analysis can be feasibly used to provide a rich description of a CBO engaging in knowledge mobilization activities.
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 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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".