Scoping Science-Policy Arenas for Regional Ocean Governance in the Wider Caribbean Region
Bibliographic record
Abstract
This paper explores the diversity of relationships that exist between science and policy and which underpin the uptake of science in oceans policy-making in the Wider Caribbean Region (WCR). We refer to these complex relationships, influenced by organizational culture and environments, as science-policy arenas. The paper examines the types of decisions that require science input, where the decision-making responsibility lies, who the science providers are, and how science gets translated into advice for a suite of 20 regional Intergovernmental Organizations (IGOs). The picture that emerges is one of a diverse suite of well-structured and active science-policy processes, albeit with several deficiencies. These processes appear to be somewhat separated from a broad diversity of potential science inputs. The gap appears largely due to lack of accessibility and interest in both directions (providers <-> consumers), with IGOs apparently preferring to use a relatively small subset of available expertise. At the same time, there is a small number of boundary-spanners, many of which are newly emerging, that carry out a diversity of functions in seeking to address the gap. Based on our scoping assessment, there is an urgent need for actors to understand the networks of interactions and actively develop them for science-policy interfaces to be effective and efficient. This presents a major challenge for the region where most countries are small and have little if any science capacity. Innovative mechanisms that focus more on processes for accessing science than on assembling inventories of available information are needed. A managed information hub that can be used to build teams of scientists and advisors to address policy questions may be effective for the WCR given its institutional complexity. More broadly, recognition of the potential value of boundary spanning activities in getting science into policy is needed. Capacity for these should be built and boundary spanning organizations encouraged, formalized and mainstreamed.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.005 |
| 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".