Knowledge exchange at the interface of marine science and policy: A review of progress and research needs
Bibliographic record
Abstract
The management of oceans and coasts needs to be informed by the best available knowledge. One way to support that is through interactive knowledge exchange (KE). Over the last decade, KE strategies have been shared with the marine research community, however, it is unclear whether this has led to recent (i.e., since 2015) progress. Through a systematic review of 60 recent academic articles applying or evaluating marine science-policy KE we synthesize trends in strategies, reasons for using a specific strategy, enablers, achievements, and evaluation. Most articles located were from North America, routinely included local actors or organizations, and spanned different governance levels. In addition to knowledge co-production and boundary organizations as well-established strategies, research networks and engaged funders coordinating and supporting science-policy KE played an increasing role. However, studies rarely provided reasons for why they adopted a specific KE approach within their given context. Achievements of KE are becoming more broadly understood and, among others, included the generation of new knowledge and impact on management or individuals. Factors that enable such achievements are a key area of progress in the literature. Individual case studies referred to the process level (e.g., practical collaboration, inclusive participation and equity, clear goals, continuity), interpersonal level (e.g., trust building, relationships, regular face-to-face contact), and individual level (e.g., skillsets, understanding, champions, facilitators). The measures to evaluate the effectiveness of KE were predominately qualitative (e.g., relevance of knowledge, use of knowledge in management, individual conceptual impacts, and level of engagement). It is increasingly understood what diversity of impacts to look for and unfold ways for more purposeful evaluation. In conclusion, much progress has been made in recent years, and we identify ten further research needs around the inclusivity, institutionalization, strategy selection, and efficiency of KE approaches to support evidence-informed ocean and coastal management.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.091 |
| 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".