Strengthening the Role of Science in the Environmental Decision-Making Processes of Executive Government
Bibliographic record
Abstract
Internationally, there is a growing call to embrace more participatory and democratic approaches to environmental science and policy to improve sustainability outcomes. This presents a particular challenge in Westminster-based systems of government, where participatory and inclusive structures for policy making are considered inherently difficult due, in part, to the high concentration of power in the executive and political elite. To better understand this challenge, we conducted exploratory research into the science–policy experiences of former environment ministers (politicians) and senior bureaucrats who have held executive roles in provincial/ state and federal governments across Canada and Australia and the national governments of New Zealand, Ireland, and the United Kingdom. Our results suggest that government organizations could further strengthen a culture of policy-relevant research and evidence-based policy on environment issues by fostering more decentralized approaches to policy and more democratic approaches to scientific knowledge production that better accounts for the complexity of environmental decision making.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.102 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.017 | 0.044 |
| Scholarly communication | 0.027 | 0.015 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".