Using proportional modeling to evaluate irrigator preferences for market‐based water reallocation
Bibliographic record
Abstract
Abstract Irrigators’ policy preferences for water reallocation programs usually take the form of proportional data, where one option will be relatively more or less favored than another in the composition of a government's total budget apportionment to address water reform. This study applies a zero‐one inflated beta regression to model Murray–Darling Basin irrigators’ preferences for market‐based water policy programs. Market‐based arrangements are more likely to provide efficient solutions to water reallocation problems, particularly where future uncertainty and appropriate pricing induce irrigator preferences for such programs. Our modeling of drivers of irrigator preferences for government expenditure on market‐based programs identified different determinants of zero (a corner solution) and proportional outcomes for the reallocation of Murray–Darling Basin water. In addition, the proportional modeling identifies some variables (namely, state regional influences, the type of farm production and recent debt, low income, or water allocation stressors) that increase engagement with market‐based programs. Interestingly, while price variables are important and statistically significant, they appear to be less relevant to program engagement than other influences.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
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".