Who trades water allocations? Evidence of the characteristics of early adopters in the Goulburn–Murray Irrigation District, Australia 1998–1999**
Bibliographic record
Abstract
Abstract This article applies a model of innovation to analyze the characteristics of irrigators within the Goulburn–Murray Irrigation District in Australia and examines the efficiency of the early water market in the late 1990s. Using multinominal and binary logit analyses we identify the factors associated with irrigators who sold or bought water allocations during 1998–1999 and irrigators who at that time had never participated in any kind of water trading. Contrary to expectations we find that early adopters of water trading were older farmers with low farm productivity, but that in line with theory they had higher levels of education, had spent less time farming, had larger irrigated area, farm operating surplus and farm assets, owned farms that were more intensively farmed, and were more progressive in their planning. There was only weak evidence to suggest that water moved from lower value uses to higher value uses, suggesting the water allocation market had limited efficiency in its’ initial years.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".