Multilevel governance and the politics of environmental water recoveries
Bibliographic record
Abstract
In the middle two quarters of the twentieth century, irrigated agriculture expanded rapidly in many parts of the world, including western Canada, southeastern Australia and the western United States (US). Much of this expansion was facilitated by government policies that directed public money towards large scale dam and irrigation project construction, as well as liberal water allocation policies that sought to get as much water as possible into productive agricultural use. By the early 1970s however, the political consensus that had supported the era of irrigation expansion was under significant challenge. The emerging environmental movement, in particular, successfully challenged the construction of many new damming projects and generally worked towards the protection and restoration of riverine environments in irrigation areas. One of their most important efforts in this regard has been their push for environmental water recoveries. Environmental water recoveries are government programs that take water originally allocated for irrigation and reallocate it for environmental protection purposes. While many environmentalists see these water recoveries as essential for creating a sustainable balance between nature and economic production, most irrigators regard them as a fundamental threat to their livelihoods and communities, and both sides have worked to advance their interests through the political process. Due to the federal nature of Canada, Australia and the United States, irrigator-environmentalist conflicts over environmental water recoveries have played out in multilevel political institutions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".