Beyond the conventional boundaries of knowledge management: navigating the emergent pathways of learning and innovation for international development
Bibliographic record
Abstract
This paper explores the relationship between knowledge management (KM) and innovation management (IM) in policy processes. By describing and analysing the roles of researchers as knowledge and innovation managers in policy processes we also contribute to the debate on how researchers can enhance their effective contribution to policy processes. Empirical data for the paper were gathered between December 2008 and November 2010. During that period, two of this paper's authors conducted participatory action research whilst supporting the Mozambican inter-ministerial Subgroup Sustainability Criteria in developing a sustainability framework for biofuel production in Mozambique. We conclude that KM and IM are mutually reinforcing and inextricably bound: KM can provide the basis for engaging in IM activities or roles, which may -- consequently -- create an enabling environment for more effective KM in policy processes. The active embedding of researchers in policy processes an action-oriented research approach and systematic reflection can enable researchers to continuously determine what (combination of) KM and IM strategies or roles can enhance the actionability of research in, and the quality of the policy process. To do so successfully, a process-based research approach and strategic management of the boundary between research and policy are key
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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; a candidate call from one teacher head, 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".