The Role of National Agencies as Honest Brokers Between Science and Policy: Case Studies on Environmental Sustainability Indicators
Bibliographic record
Abstract
Abstract National agencies play important roles in developing the mission of their government and the vision of their nation. This review provides two examples of how national agencies, in particular, national statistical agencies and national environmental agencies are engaged in proactive interpretation of data and the role they play in the science‐policy interface, the interface where impartial evidence and values interact. The first example presents the science‐policy interface through experiences gained during the design, development, and dissemination of the Canadian Environmental Sustainability Indicators. The second example presents the science‐policy interface through experiences gained from understanding the issues related to the Pollution Standard Index of Singapore. Both examples highlight the means by which particular environmental sustainability indicators, for example, air quality, are collated, analyzed, and disseminated to the public. The examples underline the important role of national agencies as catalysts for honest brokering in the interface between science and policy.
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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 | 0.051 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".