Do Recent Amendments to Alberta's Municipal Government Act Enable Management of Surface Water Resources and Air Quality?
Bibliographic record
Abstract
Since 2015, new provisions have been added to Alberta’s Municipal Government Act (MGA) that arguably authorize municipalities to manage components of the environment, such as surface water resources and air quality at the local and regional geopolitical landscape scales. Since 2013, Part 17.1 enabled voluntary formation of “growth management boards” (GMBs) by two or more participating municipalities, and once appointed by the Minister, GMBs are empowered to create “growth plans” to govern growth-related land use decision-making processes within the boundaries of the participating municipalities. Part 17.1 was amended in 2016 and new regulations followed in 2017. City Charter provisions enacted in 2015 give broad governance powers to cities. MGA provisions that create both these new institutional arrangements do not preclude GMBs or cities from developing municipal environmental management objectives. Recent additional MGA amendments enacted as the Modernized Municipal Government Act (MMGA) in December 2016, and further amendments in the spring of 2017 added a preamble, defined “body of water” for the purposes of the MGA, provided for intermunicipal collaborative governance of land use, and amended the environmental reserve provisions and other regulatory aspects of Part 17: Planning and Development. Two new purposes of municipal government were added: “to work collaboratively with neighbouring municipalities to plan, deliver and fund intermunicipal services,” and “to foster the well-being of the environment.” In this article, amendments to the MGA since 2015 are examined and analyzed in light of Alberta’s regional watershed scale land use policy, legislation, and regulations to determine if Alberta municipalities are now authorized to manage the environment, specifically surface water resources and water quality.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".