Principles for scientists working at the river science‐policy interface
Bibliographic record
Abstract
Abstract In the face of mounting environmental and political challenges in river management, accurate and timely scientific information is required to inform policy development and guide effective management of waterways. The Murray–Darling Basin is Australia's largest river system by area and is the subject of a heavily contested series of water reforms relying comprehensively on river science. River scientists have specialised knowledge that is an important input into evidence‐based decision‐making for the management of the Murray–Darling Basin, but despite extensive literature on the interface between science and policy, there is little guidance on achieving policy relevance for practicing scientists. Here, we provide a set of important discussion points for water scientists to consider when engaging with policy‐makers and environmental water managers. We place our considerations in the context of a broader literature discussing the role of natural‐resource scientists engaging with policy and management. We then discuss the different roles for river scientists when engaging in this space, and the advantages and pitfalls of each. We illustrate the breadth of modes of engagement at the science‐policy‐management interface using the Murray–Darling Basin as an example. We emphasise the need for effective governance arrangements and data practices to protect scientists from accusations of operating as advocates when working to inform management and policy.
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 | 0.400 | 0.270 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.030 | 0.155 |
| Scholarly communication | 0.054 | 0.046 |
| Open science | 0.015 | 0.043 |
| Research integrity | 0.081 | 0.076 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".