THE USE AND INFLUENCE OF SCIENTIFIC INFORMATION IN ENVIRONMENTAL POLICY MAKING: LESSONS LEARNED FROM NOVA SCOTIA
Bibliographic record
Abstract
Governmental organisations produce vast quantities of scientific information on the state of the marine and coastal environment which is often intended to guide policy-making to mitigate or reverse the declining trends in the health of the environment. How scientific information is used and how it influences environmental policy and decision making are however not well understood. The apparent disconnect between the knowledge and information produced by scientists and that used by policy makers is attributed to problems at the science-policy interface. Based on a multi-disciplinary literature review, this paper describes how policy makersseek out and use scientific information within the context of policy design in the 21st century. Best practices for increasing information flows across the science-policy interface are drawn from a study of the awareness, use, and influence of The 2009 State of the Nova Scotia Coast Report in coastal policy making in Nova Scotia.Strategic or rational approaches to policy making can increase the two-way flow of information across the science-policy interface as it facilitates collaboration among multiple actors in information generation, transmis-sion, and use. The production, use, and influence of The 2009 State ofNova Scotia's Coast Report in coastal policy making in Nova Scotia demonstrates the strategic approach to policy making whereby coastal policy is being developed through (i) intergovernmental partnerships, (ii) the use of best available information, (iii) linkages between the policy process and policy output, and (iv) public participation.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".