Identifying common traits among Australian irrigators using cluster analysis
Bibliographic record
Abstract
In Australia there is a growing awareness that the over-allocation of water entitlements to irrigators needs to be reduced so that environmental flow allocations can be increased. This means that some water will need to be acquired from irrigators and returned to the environment. Most current water reform policies assume that irrigators are solely motivated by profit and will be willing sellers of water, but this might be an untenable approach. Authorities will need to consider new ways of encouraging the participation of irrigators in water reform. The main aim of this research was to identify the non-commercial influences acting on irrigators' behaviour, especially the influence of the values that they hold toward family, land, water, community and lifestyle. The study also aimed to investigate whether it is possible to group irrigators according to these values and then use the groupings to describe how these might affect their willingness to participate in environmental reforms. We clustered the irrigators into three groups with differing orientations; (i) Investors [25%]-profit oriented, (ii) Lifestylers [25%]-lifestyle oriented, (iii) Providers [50%]-family-succession oriented. This research indicates that when designing policy instruments to acquire water for environmental purposes policy-makers should pay more attention to the factors influencing irrigators' decision making, especially non-commercial factors.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".