Bibliographic record
Abstract
Abstract. In 1996, the Alberta legislature passed the Water Act , a landmark piece of legislation that introduced a number of significant water policy reforms, including a variety of eco-support instruments: regulatory mechanisms that can be used to define and preserve a share of water for environmental protection and restoration purposes. This article explains the inclusion of eco-support instruments in the Water Act by combining the Advocacy Coalition Framework with Joseph Nye's distinction between “hard” and “soft” power. It identifies two main advocacy coalitions in the Alberta water policy subsystem, the “Greens” and the “Aggies,” and argues that the development of the Water Act can be characterized as a contest between Green soft power and Aggie hard power. Accordingly, the inclusion of eco-support instruments in the Water Act was the result of the Greens' newfound soft power, but the Aggies' enduring hard power ensured that more radical reforms were not undertaken. Résumé. En 1996, la législature de l'Alberta a adopté la loi sur l'eau, un projet de loi historique qui a introduit un certain nombre d'importantes réformes de la politique de l'eau, y compris une variété de l'éco-instruments de soutien: les mécanismes de régulation qui peuvent être utilisés pour définir et préserver une part de l'eau pour protection de l'environnement et à des fins de restauration. Cet article explique l'inclusion de l'éco-instruments de soutien à la loi sur l'eau en combinant l'Advocacy Coalition Framework avec distinction Joseph Nye entre «hard» et «soft» de puissance. Il identifie deux principales coalitions de plaidoyer dans le sous-système de l'Alberta, de la politique de l'eau “Verts” et le “Aggies, et affirme que le développement de la loi sur l'eau peut être caractérisé comme un concours entre soft power Vert et hard power Aggie. En conséquence, la prise en compte de l'éco-instruments de soutien à la loi sur l'eau a été le résultat des Verts soft power retrouvée, mais les Aggies énergie durable dur veillé à ce que des réformes plus radicales ne sont pas prises.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.019 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".