Informing water harvesting technology contract design using choice experiments
Bibliographic record
Abstract
Abstract Introducing water harvesting technology is expected to be more effective and last longer if farm households are involved in their design. The main objective of this study is to inform policymakers in Ethiopia about the most important terms and conditions to incentivize farmers to enter into a contractual agreement to invest in water harvesting on their land. In order to test the influence of the way the specific contractual terms and conditions are communicated to farm households, many of whom are illiterate, a split sample approach is applied with and without visual aids for technical, institutional, and economic contract characteristics. Both samples generate significantly different results, highlighting the importance of how information is conveyed to farm households. This pattern is confirmed when examining the self‐reported importance attached to the various contract characteristics. Equality Constrained Latent Class models show that contract characteristics for which visual aids were developed are considered more attentively, emphasizing the importance of adequate communication tools in a developing country context where literacy rates are limited to increase water technology innovation uptake and reduce farm household vulnerability to droughts.
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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 | 0.050 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".