Taking knowledge exchange to practice: A scoping review of practical case studies to identify enablers of success in environmental management
Bibliographic record
Abstract
Abstract A gap exists in the literature on how to implement theories of knowledge exchange (KE) into practice within an environmental management context. To support the improved practice of KE, we conducted a scoping literature review evaluating 56 empirical case studies globally to identify enabling conditions for implementing effective KE. Identified enabling conditions were organized into a core capacities framework, which highlighted essential elements of effective KE from organizational, individual, financial, material, practical, political, and social capacity dimensions. Results show that major enablers to effective KE relate to practitioners' individual and organizational capacity including the ability of practitioners (often boundary spanners) to establish trust with relevant actors through their interpersonal relationships and possessing sufficient background knowledge and skills to facilitate collaborations across disciplines and sectors. We also identified main challenges to engaging in KE (e.g., insufficient long‐ term funding for projects, lack of interpersonal skills for KE practitioners to build relationships and network, and inadequate background knowledge for practitioners to exchange knowledge in an accessible manner), and the outcomes and impacts that can emerge from effective KE work. We find that practitioners often perform quantitative evaluations that provide instantaneous and measurable impacts for the effectiveness of KE, but do not capture the impact of interpersonal relationships and trust that are best achieved through qualitative approaches. Lastly, the synthesis of enablers, challenges, outcomes, and impacts presented in this paper can be a resource for practitioners to identify what enablers may be missing from their KE strategies and in what capacity the KE work can be strengthened.
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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 | 0.122 | 0.262 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.034 | 0.030 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".