Environmental science and public policy in Executive government: Insights from Australia and Canada
Bibliographic record
Abstract
This paper presents the results of an exploratory study into the science–policy experiences of former Environment Ministers (senior politicians) and Department Secretaries/Deputy Ministers (senior public servants) to better understand the role of science-based knowledge in the Executive decision-making processes of Westminster-based governments. Our participants identified a number of factors affecting the value of science-based evidence to strategic public policy processes. They described a lack of access to appropriately contextualized knowledge and a lack of accountability to demonstrate how science was considered in Cabinet decision-making. Many participants felt senior academics had an obligation to be more involved in public policy debates, to advocate policy positions based on their research and to ask questions that could assist governments on environmental issues. Concomitant was the desire for fundamental institutional changes, including greater use of deliberative public participation tools in environmental science and policy and more networked approaches to science.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.033 | 0.012 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".