Knowledge brokers in a knowledge network: the case of Seniors Health Research Transfer Network knowledge brokers
Bibliographic record
Abstract
BACKGROUND: The purpose of this paper is to describe and reflect on the role of knowledge brokers (KBs) in the Seniors Health Research Transfer Network (SHRTN). The paper reviews the relevant literature on knowledge brokering, and then describes the evolving role of knowledge brokering in this knowledge network. METHODS: The description of knowledge brokering provided here is based on a developmental evaluation program and on the experiences of the authors. Data were gathered through qualitative and quantitative methods, analyzed by the evaluators, and interpreted by network members who participated in sensemaking forums. The results were fed back to the network each year in the form of formal written reports that were widely distributed to network members, as well as through presentations to the network's members. RESULTS: The SHRTN evaluation and our experiences as evaluators and KBs suggest that a SHRTN KB facilitates processes of learning whereby people are connected with tacit or explicit knowledge sources that will help them to resolve work-related challenges. To make this happen, KBs engage in a set of relational, technical, and analytical activities that help communities of practice (CoPs) to develop and operate, facilitate exchanges among people with similar concerns and interests, and help groups and individuals to create, explore, and apply knowledge in their practice. We also suggest that the role is difficult to define, emergent, abstract, episodic, and not fully understood. CONCLUSIONS: The KB role within this knowledge network has developed and matured over time. The KB adapts to the social and technical affordances of each situation, and fashions a unique and relevant process to create relationships and promote learning and change. The ability to work with teams and to develop relevant models and feasible approaches are critical KB skills. The KB is a leader who wields influence rather than power, and who is prepared to adopt whatever roles and approaches are needed to bring about a valuable result.
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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 | 0.038 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.026 | 0.021 |
| Scholarly communication | 0.017 | 0.023 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".