The future of knowledge brokering: perspectives from a generational framework of knowledge management for international development
Bibliographic record
Abstract
Knowledge brokering has a crucial role in the field of international development because it is able to act as a cognitive bridge between many different types of knowledge, such as between local and global knowledge. Much of the research on knowledge brokering has focused on knowledge brokering between research, policy and practice, rather than looking at its wider implications. In addition, there appears to be no literature on the future of knowledge brokering, either within or outside the development sector. Given the apparent absence of literature on the future of knowledge brokering, a discussion group was held with experts in the field of knowledge management for development (KM4D) in April 2017 to consider their opinions on the future of knowledge brokering. Their opinions are then compared to the generational framework of KM4D, developed in a series of iterations by researchers in mainstream (non-development) knowledge management (KM) and KM4D researchers. In this framework, five generations of KM4D with different key perspectives, methods and tools have been identified. Based on the inputs from the experts in the discussion group, the future of knowledge brokering practice in international development appears to resemble practice-based, fourth generation KM4D, while there is some evidence of the emergence of fifth generation KM4D with its more systematic, societal perspective on knowledge. Given that the Sustainable Development Goals are providing a universal framework which is relevant to both organizational and societal KM4D, a new systemic conceptualization of KM4D is proposed which brings both of these strands together in one integrated framework linked to the SDGs. The SDGs also support the call for a new knowledge brokering practice with a greater emphasis on brokering knowledge between organizational and societal actors.
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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 | 0.017 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.033 |
| Scholarly communication | 0.018 | 0.026 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.006 | 0.005 |
| 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".