Using a Choice Experiment to Improve Decision Support Tool Design
Bibliographic record
Abstract
Abstract The potential for computer‐based decision support tools (DSTs) to better inform farm management decisions is well‐recognised. However, despite considerable investment in a wide range of tools, uptake by advisers and farmers remains low. A greater understanding of the demand and the most valued features of decision support tools could improve the uptake and impact of DSTs. Using a choice experiment, we estimated the values that Australian farm advisers attach to specific attributes of decision support tools, in this case relating to weed and herbicide resistance management. Results from discrete choice models showed that advisers' preferences differ between private fee‐charging consultants, those attached to retail outlets for cropping inputs, and advisers from the public sector. Advisers valued ‘reliable accurate results’, and also placed a consistently high value on models with an initial input time of three hours or less, compared to models that are more time demanding. Results from latent class models revealed a large degree of preference heterogeneity across advisers. Although the majority of advisers valued DST output that is specific to individual paddocks, approximately one‐quarter of the respondents preferred generic predictions for the district rather than predictions with greater specificity. The choice experiments helped to identify the attributes most valued by advisers in different market segments. This has implications for those seeking to influence decision‐making by allowing DST development to be better targeted towards the preferences of potential users.
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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 | 0.184 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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".