Exploring links between policy preferences for water reallocation and beliefs, values, attitudes, and social norms in Alberta, Canada
Bibliographic record
Abstract
In many semiarid regions, total water allocations exceed levels available for extraction.Despite growing demand for water from urban and environmental uses, the majority of these allocations are held by agricultural users.In order to meet new demand in the face of uncertain future supply, water must be reallocated from irrigation to urban and environmental uses; however, such reallocation faces stiff opposition from irrigators and non-irrigators alike.Although irrigators have disproportionate power over the reallocation process, the preferences of non-irrigators with greater electoral power and contributions to tax revenue are also important to policy makers.This study explores these issues based on extensive surveys of non-irrigators in Alberta, Canada.Values, beliefs, and attitudes are found to influence policy preferences differently.Policy makers and water managers should consider these psychological constructs when designing, marketing and implementing policies and mechanisms to reallocate water in accordance with the values of wider society.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.000 |
| 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".