Knowledge Mobilization in Research With Equity- and/or Sovereignty-Deserving Communities: A Bibliometric Analysis Protocol
Bibliographic record
Abstract
Introduction: Knowledge mobilization (K*)—a term encompassing activities such as synthesis, dissemination, exchange, and application of knowledge—is discussed and cited across disciplines, particularly in research addressing equity- and/or sovereignty-deserving communities. Despite increasing calls for applied research, significant gaps remain between knowledge generation and its outcomes. Community-based participatory research (CBPR) aims to narrow this gap, especially in contexts marked by historical and systemic exclusion. Objectives and Methods: The objectives of this bibliometric analysis are to examine how K* terminology is applied, cited, and connected across disciplines, geographies, authors, and journals, and to produce accessible visual data that highlights patterns related to equity- and/or sovereignty-deserving communities. Adhering to established bibliometric methods, we will use Covidence to pre-screen records, export relevant records for a search within the Web of Science Core Collection to generate data, and then analyze and visualize citation and authorship trends and keyword occurrences using Excel, VOSviewer, and Gephi. Results and Discussion: The findings will reveal the frequently used K* terms, their citation patterns, and how they cluster across disciplines, geographies, authors, and journals. Network visualizations will highlight influential citations, recurring keywords such as equity and community engagement, and thematic intersections with research involving equity- and/or sovereignty-deserving communities. The bibliometric analysis will contribute critical insights into how K* is framed and interacts with equity- and/or sovereignty-deserving communities within the literature. Conclusion: Our protocol serves as a replicable guide for future bibliometric analyses in this area. By leveraging systematic searching protocols and the rigour of bibliometrics, we can create data visualizations to map influence, reveal hidden connections, and present complex knowledge landscapes in ways that are both analytically robust and accessible to diverse audiences, including equity- and/or sovereignty-deserving communities.
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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 | 0.217 | 0.321 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.037 | 0.046 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.048 | 0.014 |
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; the direct Gemma label and the distilled Codex classifier 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".