Applying user-centred design to climate and environmental tools
Bibliographic record
Abstract
The number of web portals and online tools to support or inform decision-making on environmental and climate issues has grown steadily in recent decades. This paper explores the benefits and challenges of applying user-centred design (UCD) in environmental tool development, drawing on three case studies at the science-policy interface. We examine the roles and perspectives of scientists, funders, software developers, and end-users, highlighting how their often conflicting objectives can lead to a lack of focus. Active management is essential to align tool development with user needs. To increase the credibility and usefulness of environmental tools, we argue for stronger adoption of UCD, greater attention to post-creation tool use, and better integration of tool development into broader project lifecycles. Finally, we recommend building on or improving existing tools and platforms rather than developing new ones for each project, fostering greater continuity, efficiency, and long-term impact in the science-policy interface.
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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 | 0.075 | 0.076 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.021 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".